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Roger Clarke's COVID IT and Modelling

IT and Modelling Applications in Support of Public Health Management
during the COVID-19 Pandemic

Draft Chapter for Wickramasinghe N. (ed.) 'Digital Disruption in Healthcare' Springer, 2022?
Version of 3 March 2021

Roger Clarke **

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When a pandemic occurs, people naturally seek ways in which information technology (IT) can play a constructive role in the public response. A foreground need is in the area of prevention and treatment of the conditions that afflict patients. The next need is for assistance in public health management, which seeks to slow the spread of the virus, protect particularly vulnerable sub-populations, ensure capacity to treat sufferers, and ultimately defeat the virus, while sustaining public confidence and achieving sufficiently high levels of compliance.

In the case of COVID-19, during the period March to December 2020, several IT initiatives were prominent. Limited value was delivered by apps using Bluetooth signal strength as a proxy-measure for proximity between people, and manual contact-tracing techniques were much more effective and efficient. Another publicly visible IT application was the gathering and publication of data. This entertained the public, but provided limited support to policy-makers in their efforts to understand the phenomena.

Data alone is not enough. To constitute information, and to enable the people responsible for public health management to make decisions, data must have context. That context may be provided by each individual policy-maker's own mental models. However, a major programme of this nature involves many stakeholders with diverse perspectives. The context is therefore multi-dimensional, it features competition among values, and the conception of the problem-space needs to be shared rather than personal.

The most powerful form of context is provided by visual models that impose some degree of formality on the problem-space, that are sufficiently graphic that all stakeholders can relate to them, and that have an associated terminology that is reasonably common among the stakeholders. Given such a model, it may be easier to identify data that would be valuable input to deliberations, to generate and evaluate alternative courses of action, and to assess both the potential and the actual impacts of interventions.

'Discrete-event simulation' (DES) models offer scope for the support of public policy-makers' decision processes, by supporting mind-experiments and conversations about the real world into which public policy-makers were injecting successive interventions and refinements of interventions. A DES model was postulated in April 2020, and refined in January 2021 on the basis of developments during the intervening 8 months.


1. Introduction

From time to time, viral epidemics within individual countries threaten the health and lives of that country's inhabitants, and may wreak havoc on social and economic activities. Once the threat has passed, recovery may be quite brisk, provided that the country is sufficiently economically open. Global pandemics, on the other hand, harbour the potential for health impacts over large regions and potentially the whole world, and may have longer-term impacts on economic wellbeing because all countries' economies have been hampered and hence drivers of recovery are in short supply.

It is only natural that information technology (IT) be considered as a means of assisting the public response to a pandemic. IT was originally seen as a tool to assist in processing data, then as a means to support decision-making at a tactical, operational level, later as a tool to be aligned with organisational strategy, and currently as a transformer of business processes and organisations and a disruptor of business models and industry sectors. Early forms of IT were subject to modest degrees of opposition on the basis that they were suspected of replacing humans and destroying livelihoods. More recent forms are accused of being dehumanising, with technology being implemented for its own sake rather than for society's. A theme in recent criticisms has been that disruption is asserted, and often merely assumed, to be beneficial. A pejorative term currently in use is 'technological solutionism' (Morozov 2013).

A century after 'The Maybe-Spanish Flu of 1920', the world was subjected to 'The Maybe-Chinese Coronavirus of 2020'. Technologists leapt into action, attracting emergency funding to enable experiments with medical tools (e.g. for infection-testing, antibody-testing, symptom treatments, discovery of the modes of transmission, spread-containment mechanisms and vaccination) and with computing tools (e.g. for contact-detection, proximity-monitoring, data management and decision support). As is to be expected of urgent, rapidly-performed experiments with available tools and the brisk conception and development of new tools, a great many projects were ineffective and short-lived. A few, however, delivered very considerable benefits to individuals, societies and economies.

This Chapter's purpose is to review the application of IT in support of public health management during the COVID-19 crisis-year of 2020. It commences by briefly summarising key features of the COVID-19 pandemic during the period March to December 2020. It then surveys various approaches that were adopted to applying IT to the perceived needs. It is concluded that many failures were a result of ad hoc applications that lacked an appreciation of context and purpose. Opportunities therefore exist to apply modelling more effectively in the area of public health management. An examination is conducted of one particular modelling approach referred to as discrete-event simulation (DES). A model is presented that was devised in April 2020. Developments in the field during the following 8 months are identified, and their implications for the model are investigated. The conclusion is drawn that this modelling approach has potentially significant benefits in public health management.

2. The COVID-19 Pandemic

A new virus first came to public notice in the form of an epidemic in the Chinese city of Wuhan in December 2019. Unsurprisingly, it took some time to be recognised and then accepted as a serious threat to public health. On 11 March 2020, based on rapid growth in detected case-numbers in northern Italy, Iran and South Korea, the World Health Organisation (WHO) declared a pandemic. By the end of March 2020, viral outbreaks had exploded in the USA, Spain, Germany and France, with rapid spread emergent in many other countries. For a time, some governments pursued a witch-hunt, seeking to blame China for originating the virus, suppressing information about the initial outbreak and/or failing to control it, thereby enabling its spread.

In most countries, there was an early peak of infections lasting 2-4 months, with deaths following after a 1-3 week lag, then a lull, then 6-8 months later a 'second wave' in many cases worse than the first (Econ 2020a). By the end of 2020, substantial second waves were infecting very large numbers of people and killing large numbers, with the cumulative (known) case-count worldwide past 80m and the death-count approaching 2m. It was clear that the scale exceeded those of all of the dozen or so pandemics of the last century except HIV/AIDS since 1980 and the 'Spanish Flu' of 1918-20. It does not appear likely to reach the scale of the successions of 'plagues' during the Middle Ages.

The cause was identified as a form of coronavirus, spread primarily by an infected person coughing or sneezing, or perhaps even speaking or breathing out, which released contaminated droplets (over a range of perhaps 1m), or possibly very small droplets or aerosols (over a range of perhaps 3-4m), or by direct contact with another person, or by contaminating 'fomites', i.e. objects and surfaces in the immediate environment (WHO 2020b).

A person with the virus could be infectious from 1-3 days before symptom onset, and then for a further uncertain period up to 3 weeks in mild to moderate cases, but much longer in severe cases (WHO 2020c). However, most people are asymptomatic, but infectious for at least 1-2 weeks, with a very low likelihood of detection, and hence with a moderately high likelihood of infecting others.

One study found the susceptibility to infection of those under 20 years of age to be half that of adults, and that clinical symptoms manifest in c.20% of infectees aged 10-19, rising to c.70% in those over 70 (Davies et al. 2020). A separate literature review suggested infection susceptibility was significantly lower under 10 years, and higher in adults aged over 60 and for adults sleeping close to an infected individual. There was some evidence of robust spread in secondary schools, but of much more limited spread in primary schools (Goldstein et al. 2020).

The proportion of detected cases requiring hospitalisation appeared to vary in the range 2-6%, the large majority being people over 70. There were no known treatments for the virus itself. Hospital staff did what they could to address each patient's symptoms, although techniques developed as experience accumulated. The proportion of hospitalised patients needing admission to Intensive Care Units (ICUs) ranged from 5% to 15%, but this statistic was also very heavily skewed towards people over 70. In a number of regions within various countries, but particularly the north of Italy and later some parts of the USA, and during both the first and second waves, ICU capacity proved inadequate, many hospitals had over 40% of their beds filled with COVID patients, and for some periods of time a few hospitals assigned their entire bed-capacity to COVID patients (Barry 2020). Lengths of stay in hospital and in ICU varied very widely, making projections of capacity requirements very difficult (Abata et al. 2020, Rees et al. 2020).

There are very substantial differences in the degree of impact on the infectee, ranging from (mostly) nil, via short-term, unpleasant but variable experiences, to very serious lung malfunction and death from that or consequential causes. Over time, it became apparent that there are small but significant numbers of people who suffer impacts for an extended period after the initial (predominantly pulmonary) impact of the virus (SWPRS 2020b). Throughout 2020, and across almost all countries, the death-rates among infectees increased by age-group. It was commonly well below 1% up to the age of 50, but 5-15% among the over-70s. Over-70s accounted for 80-90% of all deaths. Many health care workers also succumbed to the disease, in many cases presumably because of the viral load arising from repeated exposure. In the USA, they made up about 1% of its over 300,000 deaths during 2020 (Gn 2020b).

Countries adopted highly varying approaches to public health management, with highly varying senses of urgency, varying levels of compliance by the public, and highly varying case-counts, fatality-counts and fatality-rates. During 2020, China, Singapore, New Zealand, Ivory Coast and Mozambique reported 3-5 deaths per million, whereas Argentina, Peru, Mexico, the USA, the UK, France, Spain and Italy reported 800-1000, and Belgium even more (WOM 2020). Reports on the case-fatality rate suggested about 2% of detected-infectees died (compared with Ebola 40-50%, MERS 34%, SARS 10% and seasonal flu <0.3%). However the range reported across various countries was substantial, from <1% to >7%. As shown in Figure 1, the deaths per million of population also varied enormously. Italy, the UK and the USA reported easily the worst results, followed by Sweden. A small number of countries all-but escaped the pandemic.

Figure 1: Deaths per Million Detected Cases for Selected Countries

Extract from OWID (2020)

The focus of public health actions was on prevention of spread, most urgently among those at greatest risk. Most countries sought to eradicate the disease, but some, at least initially, preferred to accommodate it and encourage the build-up of resistance so as to more quickly reach 'herd immunity'. High rates of hospitalisation and of death were experienced among the aged, especially those with prior bronchial or other conditions. Employees in hospitals and aged care homes were at risk of high viral load, and high-quality hygiene and personal protective equipment (PPE) were essential.

The public health imperative is constrained by the limitations of enforcement powers and resources, in many jurisdictions by a lack of political will, and by various factors within the broad area of population management - particularly the importance people attach to freedom of action and freedom of movement. Public health needs also conflict with the aims of economic management, at both the microeconomic level (on business enterprises, workers and their dependants) and the macroeconomic (e.g. negative multiplier effect, economy slowdown).

Information was gathered by public health agencies, and maintained, analysed and disseminated by a wide range of public sector, private sector, public interest and media organisations. Unsurprisingly given the circumstances, the quality of data was in many cases not high. The quality of analyses was also variable. Some media reporting exaggerated aspects of the pandemic, as part of their normal business of getting attention and then leveraging it. There were few reports of hysterical responses by the public, however, with the most commonly-reported instance being 'panic buying' at supermarkets, including irrational excess purchasing of toilet paper.

In most countries, there appeared to be widespread acceptance of, and reasonable levels of compliance with, public health measures, although some were afflicted by higher levels of scepticism than others. In some population-segments, there was disbelief about the existence or severity of the virus, but more generally the concerns related to particular measures such as the wearing of face-masks. In some countries, there was a degree of open opposition, including demonstrations, street-marches, and open violation of constraints imposed by public health agencies, most markedly among far-left and far-right groups.

From the first quarter of 2020 onwards, there was progressively increasing recognition that the pandemic was a serious threat to society. The following section reviews activities that involved information technology (IT).

3. IT Applications during the COVID-19 Pandemic

A variety of approaches were apparent to the application of IT in support of the worldwide effort to combat the pandemic. An early review is in Budd et al. (2020). This section identifies and briefly discusses the most prominent of these initiatives.

Considerable hope was vested in support for contact-detection and -tracing by means of apps that used Bluetooth Relative Signal Strength Indicator (RSSI) readings, as detected by mobile-phones and tablets, as a proxy for proximity between the individuals they belonged to (Ahmed et al. 2020). Some of these experiments derived from marketing applications of RSSI, and others may have been revivals of predecessor experiments in relation to flu transmission, using mobile phones for proximity-detection by means of Bluetooth and GPS-based location-data (Yoneki 2011).

Projects were implemented throughout the world, with great fanfare and high hopes. They reflected several different models, characterised by Riemer et al. (2020) as Surveillance Tracing (adopted in China, Israel and South Korea), Centralised Proximity Tracing (e.g. in Singapore and Australia) and Decentralised Proximity Tracing (notably the Apple-Google Privacy-Preserving Contact Tracing approach - AG 2020). All such apps faced enormous practical challenges, variously of a technical nature (Clarke 2020a, Mussared & Teague 2020), a social nature (Rowe et al. 2020), and a collective action governance nature (Riemer et al. 2020). As a result, they have contributed very little to contact-tracing (de la Garza 2020). Multiple jurisdictions ran very effective contact-tracing processes, but it is difficult to find any that materially benefitted from the use of a proximity-based app. The technical difficulties may well be intractable; however, leaving aside the question of whether the app works, Trang et al (2020) conducted a survey-based assessment of the app specifications that appear to be salient in order to achieve mass acceptance.

A second use of handheld apps was to photograph a QR-Code and submit pre-recorded personal details into centrally-stored venue-attendance registers (Nguyen 2020). Such technically simple schemes may be capable of reasonably high degrees of effectiveness, but they proliferated, and the data security and privacy profiles of the initatives gave rise to considerable cause for concern (Greenleaf 2020).

As the prospect loomed of vaccines becoming available, a third category of COVID-related apps was mooted. It was postulated (rather than demonstrated) that 'vaccination passports' would provide reliable evidence that the person holding the mobile device had been vaccinated. The primary purpose of vaccination is to provide the vaccinated individual with protection against being afflicted by a harmful dose of the infection. It remained unclear whether vaccination could prevent a person from spreading the virus, or, more realistically, to what extent it would reduce their capacity to spread the virus. Nonetheless, governments, the international airlines association (IATA), and tourist industry associations, grasped at the notion of a 'travel pass' (IATA 2020), in some cases even conceived as a generic tool to provide evidence of vaccination against other diseases as well (ALI 2020). The early indications were that these proposals were as poorly thought-through as the apps based on RSSI and QR-codes, and the associated contact databases and venue-attendance registers (PA 2021, WHO 2021, RS 2021).

Underlying all such initiatives was the use of IT for the facilitation and management of data collection, data processing and database maintenance. Large numbers of systems rapidly emerged, variously through new development and the re-purposing of existing applications and apps. They evidenced enormous diversity, of data-items, of data definitions and of business processing specifications, resulting in limitations on inter-operability and on the processing and analysis of data collections.

In a few cases, privacy sensitivity was built into such problem analysis as may have been undertaken, into process and data-model design, into system configuration, and into data storage. Most schemes, however, were flung together not only in haste, but with a very tight focus on the intended functionality, and little awareness of contexts of use. As a result, many such schemes involved centralised databases, and inadequate infrastructural, organisational and legal protections against second-party function creep and third-party access. A further factor in public distrust of these schemes was the strong impression they conveyed that such privacy safeguards as the law appeared to provide were able to be quickly and easily jettisoned, provided that an urgency and/or public security pretext existed or could be contrived. In some countries, the involvement of national security agencies in the process exacerbated public suspicions. Concerns arose about the routinisation and embedment of population surveillance (Greenleaf & Kemp 2020, Clarke 2020b).

As data became available, there was a rapid emergence of dynamic and entertaining graphical representations. A feature of a great many of these was that the high quality of the display obscured the low quality of the data and/or the limited relevance of the information. An example of exciting but irrelevant presentation is the "racing bar chart" animation in Figure 2, showing "Case number running totals by country", at Flourish (2020).

Figure 2: A Still from the Flourish 'Racing Bar' Animation

'Case numbers' is not a meaningful or reliable metric, because it is actually 'Detected Case numbers', and it is very challenging to devise a reliable estimator for 'Undetected Cases' and hence for 'Total Infectees'. Further, the 'Detected Cases' counts are generally not comparable, because the testing regimes, methods and population-samples varied enormously over both space and time, even within individual jurisdictions, let alone across countries. These variations appear likely to be a significant factor in the enormous inter-country differences in the proportions of 'cases' who were admitted to hospital, who were admitted to intensive care units (ICUs), who survived, and who died.

Another misleading metric, which was championed for a time during April 2020 as "the one COVID-19 number to watch", was "the growth factor", as measured by the number of new cases reported on any one day in relation to those on the previous day (Elvery et al. 2020). Unless the sample tested is both purpose-selected and consistent, it is completely unclear what the positive-test ratio indicates. Case recognition is dependent on highly varied testing regimes, methods and population-samples. There is even debate about what constitutes 'a case of COVID-19' (Heneghan & Jefferson 2020). Yet 'case-count mania' continued as late as December 2020, with more useful information often marginalised.

Another inadequacy was the reporting of metrics on a per-country basis, despite the enormous disparity in countries' populations. To make comparisons, a normalised basis was needed, such as counts per million, or per 100,000. In addition, given the disparity of impact, there was a need to distinguish among sub-populations to reflect the degree of risk of infection and of serious health consequences.

Exemplary cases existed in which data of reasonable quality was presented graphically in a manner that provided both a sense of proportion and the scope to undertake more careful analyses. One was numbers and rates of 'excess deaths', also called 'excess mortality'. This is the number of deaths above the number expected on the basis of prior data, in some cases expressed as a proportion of the expected death-count (Econ 2020b). One study found that, for 15 weeks from mid-February 2020, it ranged across 21 countries from slightly negative to +100 deaths per million. This reflected additional deaths arising directly from COVID, or indirectly (e.g. failure to go to hospital after a heart-attack because of a fear of catching COVID), but also fewer deaths as a result of COVID (e.g. due to reduced time on the road) (Kontis et al. 2020). Another study of the timeline through 2020 has shown considerable variability across countries (Giattino et al. (2020). A UK Office of National Statistics graphic conveying the relevant information is in Figure 3 (ONS 2020). This clearly shows both the 'excess deaths' from COVID during the two waves, and the 'reduced deaths' from causes other than COVID.

Figure 3: Deaths Registered by Week, England and Wales, for Calendar 2020

Excess mortality is one metric that can be of value in understanding the relevant real-world system. Further categories of potentially valuable data are identified in Table 1. Most of this data was, and is, difficult even to capture, let alone to capture reliably and accurately. On the other hand, an assessment of the potential value of such data represents useful input to the question of what data should be prioritised for reliable collection.

Table 1: Potentially Valuable Categories of Data

Death counts, distinguished into sub-sets, depending on whether COVID-19 was:

•   the cause of death

•   a significant factor in the death

    (e.g. compounding prior conditions)

•   otherwise known to be present at death

•   assumed present at death

•   not known to be present at death

Death rates related to:

•   normal death rates at the time-of-year

    (in order to estimate 'excess deaths')

•   population-size (to enable comparisons

    between geographical areas and jurisdictions)

Results of tests for presence of the virus, using random samples:

•   of the whole population

•   of specific at-risk populations

Proportions tested for presence of the virus:

•   of the whole population

•   of specific at-risk populations


•   admissions

•   successful and unsuccessful discharges


•   admissions

•   successful and unsuccessful discharges

Results of tests for presence of virus-specific antibodies (as a proxy for prior exposure, for prior infectiousness, and/or for immunity), using random samples:

•   of the whole population

•   of specific at-risk populations

For targeted action to halt the spread of the virus, at-risk segments need to be postulated, and sampling conducted within those segments. Segments might be defined by geographical area, by age, by gender, by predisposing, chronic or debilitating condition (particularly bronchial and cardiac), by type of accommodation (with particular attention paid to communal and other close living, such as dormitories, prisons and aged care homes), and by intensive-exposure circumstances such as hospitals.

On the one hand, there has been evidence of poor-quality data and poor-quality analysis, particularly where the data-gathering and data analysis was ad hoc or opportunistic. On the other hand, some of these activities have delivered value. The following section considers the question as to whether the seeming absence of an 'enterprise model' of the undertaking and of 'data models' or 'information architecture' to support it, have hampered the potential contribution of IT, and hence whether return on investment in IT in the public health management context can be improved by applying insights from modelling theory and practice.

4. Modelling

This section outlines the nature and uses of models, the identifies and outlines key instances of modelling techniques that have been, or may valuably be, applied to public health management.

4.1 Modelling and Its Uses

A model is a simplified representation of a real-world system, which reflects the interdependence among entities, structures and processes. Real-world socio-economic systems are open, complex and highly inter-connected. Simplification necessarily involves limiting the scope of the model, by placing the focus on one sub-system or two or more closely-related sub-systems, at one particular level of abstraction, and by excluding some factors and using proxies for others. A model therefore cannot replicate the real-world system (von Bertalanffy 1968). However, if key factors are appropriately reflected, experimentation with it can deliver insights. At the very least, it can suggest what data might be the most valuable to collect. In addition, participation in the modelling process may enhance each observer's understanding of the world, and assist in making decisions about actions to take.

Early applications of computing to administrative data, from 1952, were characterised as data processing systems (DP). Applications that extracted and reported data of assistance in operational activities, from c.1970, were referred to as management information systems (MIS). Then decision support systems (DSS) used available data from operational support systems, combined with hypothetical or synthetic data, to enable 'what-if' investigations, and hence support strategic rather than tactical activities. The implicit models that had lain hidden beneath DP and MIS were no longer adequate. Strategic thinking demands greater clarity about models of the relevant current and possible future realities: " ... DSS ... became characterized as interactive computer based systems, which help decision makers utilize data and models to solve unstructured problems" (Sprague 1980, p.1).

During the first quarter of 2020, it became clear that COVID-19 had a high infection-rate and was life-threatening for some categories of people. As the epidemic in Wuhan developed into a pandemic, models were applied by a variety of people in a variety of contexts, in an endeavour to support various decision-makers. Table 2 identifies categories of decision-making that can be usefully distinguished, starting with the deepest-nested and most-detailed views of the phenomena, and moving towards the most abstract.

The focus of the work reported here is on the mid-levels of Table 2, primarily 4. Public Health Management, but to some extent also 3. Health Facility Management and 5. Population Management.

Table 2: Categories of Target-Markets for Modelling


Areas of Investigation and Decision-Making



•   The Virus

•   Its Micro-Impacts on on Human Physiology

•   Its Macro-Impacts on Individual Patients

•   Biological Interventions



•   Diagnosis

•   Disease Pathways

•   Medical Interventions


Health Facility Management

•   Human

•   Equipment

•   Space

•   Supplies


Public Health Management

•   Procedural Interventions

•   Separation

•   Surface-Disinfection

•   Isolation

•   Quarantine

•   Movement Control

•   Lockdown


Population Management

Social Phenomena:

•   Acquiescence, Disbelief, Misinformation,

    Civil Disobedience, Obstruction, Sabotage

Social Interventions:

•   Contact Tracing, Moral Suasion, Propaganda,

    Legislation, Law Enforcement


Economy Management

Economic Phenomena, including:

•   Consumer Spending, Corporate Spending,

    Business Enterprise Contraction and Expansion,

    Joblessness, Loss of Income

Economic Interventions, including:

•   Propaganda, Economic Stimulus, Business Protection,

    Income Protection, Economic Recovery Investments

The focus of this chapter is on the needs of public health management during epidemics. The following sub-section outlines the primary forms of modelling technique that may offer value in addressing those needs. It concludes that the conventional epidemiological models are too narrow in their focus to satisfactorily support the needs of public health management as outlined in Table 2. This suggests that the opportunity exists for more substantial contributions by applying other modelling techniques.

4.2 Modelling Techniques

Multiple forms of modelling exist. At the strategic level, the technique of System Dynamics is appropriate for the study of the behaviour of natural systems in such areas as animal populations and natural resources, but also of human-made systems such as industry sectors (Brailsford et al. 2014, Brailsford & Hilton 2001). Systems can be modelled using simple graphical conventions, with symbols for stocks, flows, converters and connectors. The technique has been applied to public health issues as varied as hospital waste management and the management of chronic diseases (Homer & Hirsch 2006, Currie et al. 2018).

The technique of Agent-Based Modelling (ABM) is applicable to circumstances in which the exercise of autonomy by individual elements within the system is a significant feature (van Dam et al. 2013). The idea is traceable to the Simula language in the early 1970s, although the term 'agent-based' was not applied until the 1990s. Discussions of its application in the public health area are in Auchincloss & Diez (2008) and Badham et al. (2018).

A further modelling approach is discrete-event simulation (DES) (Allen et al. 2015). DES modelling involves the identification of the various states that an entity (in this case a person) may be in at any given time, the transitions that they go through to pass from one state to another, and the conditions that determine when transitions occur (Sadsad & McDonnell 2014). A systematic review of publications on DES in health care, in Zhang (2018), concluded that DES has "rich potential ... to provide a broader picture of ... health care systems behavior" (p.9).

However, the kinds of models whose use was particularly evident during the pandemic were epidemiological models, most commonly of the SEIR(D) family. Many documents that report on applications of that model provide no operational definitions for the key concepts. They use the terms as though they were so well-understood, and so consistently applied, that declaration of their meaning, and discussion of those meanings' appropriateness to purpose, was redundant.

The primary reference appears to be Aron & Schwartz (1984). The underlying SIR model may have arisen from, or at least may be related to, Kermack & McKendrick (1927). Aron & Schwartz postulate that the population consists of four groups:

Occasionally added, particularly in the case of Ebola (Weitz & Dushoff 2015):

Variants that are apparent include:

Leakage from each state is sometimes acknowledged, in the form of a proportion of the count who die while within that state. However, there is commonly little or no discussion of the meanings of the terms, or of their relationship to the real world. In particular, the definitions of Q and J in these models may not accord with those used in the field.

In Silva et al. (2020), an endeavour is made to use the SEIR model to investigate "scenarios of social distancing interventions ... with varying epidemiological and economic effects: (1) do nothing, (2) lockdown, (3) conditional lockdown, (4) vertical isolation, (5) partial isolation, (6) use of face masks, and (7) use of face masks together with 50% of adhesion to social isolation" (p.1).

In Nikulin et al. (2020) the extended SEQIJR frame in Brauer (2008) was applied to model "the mitigation of the [COVID-19] virus via quarantine/isolation measures" (p.18). The authors also trialled a HIRD model (Healthy, Infected, Recovered, Dead) (pp.44-46). Giordano et al. (2020) devised a new form of model with "eight stages of infection: susceptible (S), infected (I), diagnosed (D), ailing (A), recognized (R), threatened (T), healed (H) and extinct (E), collectively termed SIDARTHE" (p.1). Another team applied established 'clinical pathway models' to assess the capacity of the Australian health system to cope with the COVID-19 pandemic. They concluded that isolation and quarantine alone would be insufficient (Moss et al. 2020).

Further, it appears that such models may only be able to represent intermediate states such as Isolation, Quarantine, Hospitalisation and ICU-Admission by means of variations in parameters, rather than by explicit representation. Moreover, it is possible that there are material differences among the meanings of the concepts as they are used in the various models, and material differences between those meanings and the definitions of the empirical data whereby comparisons between model results and real-world behaviours are made.

In summary, the SEIR(D) family of models lacks attributes necessary to support public health management, including:

In Schipper (2020), it is argued that modelling failed us during the COVID pandemic, because "the current epidemic model is medical, and narrowly so", "the current model recognizes the existence of cultural and other 'external' factors, but does not allow them to influence the model's projections or guidance", and "the model does not consider, let alone factor in even the direct health consequences of major economic disruption" (pp. 7-9). The author called for "a next generation epidemic model that goes beyond our current model ... to include:

  1. Measures of all cause morbidity and mortality;
  2. Measurable parameters of culture, geography and physical structure;
  3. Measures of economic impact; and
  4. A lexicon of common language" (p.11).

This paper adopts the position that appropriate support by IT for public-policy decision-makers in dynamic contexts like a pandemic depends on the appropriate application of modelling techniques. This depends on careful definition of the system scope, and the level of abstraction at which the system is being observed.

This paper has as its focus level 4 of Table 2, Public Health Management. Key requirements are the establishment and progressive adaptation of a model that clearly distinguishes start-point(s), states, transitions, and end-point(s), and that identifies key attributes of each individual passing through the model (age-range and relevant-prior-conditions, for example), and the postulated distributions of those variables. The following section postulates a DES model that is intended to support public health policy-makers. The adequacy of that initial model of April 2020 is then tested against the phenomena and interpretations of them reported during the remaining 8 months of 2020, and adaptations are proposed in an endeavour to improve the model's capacity to assist policy-makers.

5. Discrete-Event Modelling for Public Health Management

This section first discusses particular needs that arose during the COVID-19 pandemic in 2020, examines the application of DES modelling to public health, and finally describes an application of it that is argued to be of benefit to policy-makers.

5.1 The Needs of Public Policy-Makers

Modelling at the Bio-Medical and Medical levels as described in Table 2 ran in overdrive through 2020, but in specialist areas with limited public visibility. Economic models were also in use. The most apparent forms, however, were epidemiological models. These are well-established. They focus on parts of the Health Facility Management and Public Health Management levels, but they reflect only a limited sub-set of the factors that must be considered even in hospitals, let alone in workplaces, out on the streets, in transport, and in entertainment venues. In addition, they are 'socio-medical scientific' models that are designed to deliver understanding, rather than 'socio-medical engineering' models that can inform and motivate decisions about interventions.

It is not clear that public health management was well-served during the critical periods of 2020. A great deal of what was projected to the public was long on data (much of which was, understandably, of mediocre to low quality) and on diagrams and graphs (which were generally of high presentation-quality, but a great many of which appeared to have dubious informational value, driven by availability of data rather than by relevance).

The focus of public health management is "population-based health protection and promotion" (Novick & Morrow 2008, p.60), with efforts "organized and directed to communities rather than to individuals", and with the prevention and control of epidemics high on the priority-list (Novick & Mays 2008, p.3). Key functions and practices are (Novick & Morrow 2008, pp. 40-47):

The target-area for modelling activities to support public health management is accordingly the processes of the spread of the disease, but in such a manner that insights can be delivered to policy-makers regarding the shape that interventions may usefully take, and their likely contribution to containing that spread.

An important distinction is made in decision theory between factors that are strategic or controllable and those that are environmental or uncontrollable. A further distinction is necessary between directly-controllable factors and those that can only be indirectly influenced. For example, published government advice, formal declarations and laws are outputs, whereas the acts of individuals are outcomes, which are influenced but not determined by advice, declarations and laws. The extent to which public behaviour is compliant with the intentions of public health managers depends on controllable factors such as expression, channels of communication and timing, and on uncontrollable factors such as attitudes to authority, perceptions of the health threat, and prior experience of government actions.

Even some of the key weapons in the public health arsenal are less directly controllable than might appear to be the case. Health care facilities generally, and aged care facilities in particular, may not be sufficiently reliable in their application of established procedures in relation to hygiene and the use of clinical-grade personal protective equipment. This is especially likely where pressures to reduce costs have dominated quality factors. Supervision of suspect-quarantine and infectee-isolation may be dependent on inadequately-trained staff, contractors or military personnel. Travel restrictions are difficult to police. Records of attendance at venues are maintained by individuals and venue-operators, and assurance of data quality and data-compatibility is challenging. The implementation of border restrictions may be haphazard where multiple agencies are involved, and particularly so in countries with two or more jurisdictional layers where, for example, borders are a national matter whereas health is a state or provincial responsibility.

Public health activities inherently involve an enormous breadth of stakeholders, and an enormous diversity of perspectives and values, spanning the social, economic and psychological dimensions. As a result, decisions are actively contested, and the decision-making processes complex and at best only modestly well-structured. The Vroom-Yetton-Jago Decision Model identifies five decision-making implementation styles (Vroom & Yetton 1973, Vroom & Jago 1988). For decisions that have significant impact and require input and 'buy-in' from many participants, the relevant two of the five are consultative (group-based but leader-decided) and collaborative (group-based and group-decided).

Models are needed that reflect the key features of real-world systems that policy-makers seek to influence. However, policy-makers are confronted by diverse views among stakeholders, and a rich choice of experts, of approaches to models, of assumptions inherent within them, and hence of the findings presented by the modellers. Further, because pandemics develop in unpredictable ways, and new information and insights become available, policy-makers' appreciation of the context is adaptive. It is therefore crucial that policy-makers develop a degree of clarity about the context in which they are working, communicate that to modellers, and update modellers on changes in their perceptions of the relevant systems.

The most effective way in which modellers can contribute is therefore to start with an appreciation of the relevant domain, become familiar with the policy-makers' initial mental models, and be sufficiently 'embedded' to detect changes in their thinking. Further, modellers must convey enough information about their purposes, their assumptions, the capabilities and limitations of their method, the nature, quality and quantum of the data that they are using, and the extent to which it has and has not been feasible to test findings against the real world. Without great care, there is a high probability of misunderstandings, and of policy-makers being misled. The following sub-section considers how a particular form of modelling can be used to address these needs.

5.2 Applications of Discrete Event Modelling

Of the several modelling techniques outlined earlier, discrete event simulation (DES) appears to have a particularly good fit with the need. It is less abstract than system dynamics, omits some of the complexities inherent in agent-based modelling, and enables some of the engrained assumptions within epidemiological models to be avoided.

There is already a track-record of DES applications within the health sector, particularly in operational settings. An example of an application to the treatment of Parkinson's disease is reported in Lebcir et al. (2017). More pertinently, Bai et al. (2018) provides a literature review of the use of DES to model ICU in hospitals. Currie et al. (2020), in discussing the application of simulation modelling technique to the COVID-19 pandemic, describe DES models as being "typically used to model the operation of systems over time, where entities (people, parts, tasks, messages) flow through a number of queues and activities. They are generally suitable for determining the impact of resource availability (doctors; nurses), on waiting times and the number of entities waiting in the queues or going through the system" (p.85). The Currie article identifies a range of potential applications of DES in the context of the pandemic. In Wood et al. (2020), a report is provided of a DES model "designed to capture the key dynamics of the intensive care admissions process for COVID-19 patients" (p.1). Bolla & Sarl (2020) model flows of COVID patients in Switzerland from home to hospital to ICU and beyond.

Research has also been published on the application of DES to broader issues than Health Facility Management, such as Jalayer et al. (2020), which models "citizens living, working, pursuing their needs and travelling inside a geographical environment" (p.3). Price & Propp (2020, pp.5-14) provides a framework for assessing the suitability of systems dynamics models in informing COVID-19 policymaking, which also has application to DES models.

In Shea et al. (2020), it is suggested that effective use of models during epidemics depends on combining expert elicitation methods and a structured decision-making framework. Rhodes et al. (2020) perceive models for policy to "blend various heterogeneous data (quantitative, qualitative, abstract, empirical) from various diverse contexts (different viruses, countries, localities, studies, historical periods) ... to enable a decision" (p.2). The authors discuss an "approach to the modelling of pandemics which envisages the model as an intervention of deliberation in situations of evolving uncertainty" (p.1). "The model, precisely because it has latitude as a space of triangulation and speculation, potentiates a working relationship, in which dialogue is made possible" (p.6)

Many researchers assume that DES models have to be fed quantitative data, and that the calculations are what matters. For example, the text elided from the p.2 quotation from Shea et al. (2020) in the previous paragraph is '[blend] into a single calculative process". This ignores the considerable limits on the usefulness of quantitative analysis in such circumstances, whether conducted mathematically or numerically by experimentation. For example, a comparison across four models of the path of COVID-19 infections in South Africa (Chi et al. 2020) found very wide variation in the models' predictions of case-counts and death-counts, highlighting the folly of reliance on any of them. Similarly, the performance of countries during 2020 was poorly correlated with the results of a 2019 study specifically of preparedness for handling an epidemic (GHS 2019).

The Rhodes et al. article overlooks the fact that the 'blending', the 'deliberation', the 'speculation', the 'working relationship' and the 'dialogue' are all highly valuable in their own right, and may offer far better value to policy-makers than unverified rules applied to mediocre-quality data in a 'calculative process'. Table 3 identifies multiple ways in which a suitable DES model can be applied in the style of a decision support system to support Vroom-Yetton-Jago consultative or collaborative policy decision-making.

Table 3: Benefits of Decision Support Applications of Discrete-Event Modelling


To the extent that the model is adequately articulated, tested for logical completeness, and checked against real-world activities, it is also capable of being used to simulate flows of people through the system, and 'stocks' of people currently in each state. This approach would need to be supplemented by a segmentation analysis, distinguishing in particular:

A big-picture view encourages questions to be considered such as:

By manipulating key parameters (such as detection-rate; the proportion needing admission to hospital and to ICU; hospital- and ICU-capacity; treatment-periods; and mortality-rates), estimates could be made of the limits to the ability of health facilities to cope, and the extent to which urgent investment in additional facilities might be necessary. Hence, in addition to the primary purpose of supporting public health management activities, the model may contribute to the adjacent levels of health facility management and population management, particularly in relation to contact-tracing and social interventions to ensure the effectiveness of quarantine and isolation measures.

5.3 The Postulated Model

An investigation was conducted aimed at understanding the extent to which a discrete-event simulation (DES) model could support public health management in the context of an rapidly-developing epidemic. During March-April 2020, I postulated a state-transition model, intended to represent the population of a jurisdiction, and the flow of individual members through various states associated with infection, hospitalisation, and recovery or death. The intention was to commence with the minimum complexity, in terms of the number of states, flows, and data about each, based on the available information about the challenges public policy-makers were addressing. The model could then be experimented with, and expanded to the extent necessary to embody a sufficiently rich understanding of the public-policy problem-space. Based on government publications and media reports, and taking account of previous SEIR models, it appeared that the model would need to incorporate about 15 states, 35 flows, definitions of the conditions determinng when transitions occurred, and data-items representing individuals' key attributes.

Although a DES model can be applied computationally, that was not the intention, because the complexities and dynamism of the relevant part of the real world are such that the results would inevitably be spurious. The intention was to produce a framing tool for the problem-space, to help policy-makers formalise their own mental models, appreciate and resolve differences among those models, experiment with the model, and draw inferences relevant to the many decisions they needed to make during the weeks and months of the epidemic.

The first iteration of the model, of April 2020, is in Figure 4. A textual description of aspects of the model was also developed. Broadly, individuals were conceived as beginning as Uninfected, with a proportion passing through Infection, possibly via Hospitalisation, and on to Immunity or Death. Each of the four broad domains was conceived as encompassing a number of states, such as being in hospital, or in ICU, or in a queue to get into one of them. Various aspects of each state required some articulation, and so did the conditions under which transitions occur between states.

Figure 4: The Model Postulated in April 2020

The model is applicable to, in principle, whatever group of people suits the needs of policy-makers. This may be the population of a national jurisdiction, or of a subsidiary jurisdiction, particularly in countries in which health care is the responsibility of constitutional sub-units such as provinces and Länder. The model comprises the following elements:

Individuals may remain in the base-state indefinitely, or may transition through any of the many paths towards one of the end-points. Some flows loop back to earlier states.

A simple example is provided by the State 'Isolated' (4). This represents the stock of people who are currently both known to be infected and considered to be still capable of spreading the virus. Each individual who enters that State remains there until one of the following specified Transition-Rules is satisfied:

Examples of Data-Items that are relevant to the State Isolated (4) and its associated Flows are details of tests conducted (including the date and the result), the estimated date on which infection occurred, key factors relevant to the potential risks the individual faces (e.g. age, or year or decade of birth, and relevant chronic conditions), key socio-economic indicators (e.g. accomodation-type), details of household members and other close contacts (names and communication channels to them), relevant location history, including the nature of the location (e.g. home/hospital/ICU), date and time of arrival and departure, and contact-points.

It is inherent in models that they include representation of only some of the aspects of real-world phenomena, and use simplified representations of other aspects. This model, for example, includes the assumption that there is a high degree of compliance by individuals with the transition-rules, and that delays in implementation of transitions are minimal. The only buffer-states that enable delays to be reflected are Detected (3), which reflects delays in locating and communicating with infectees, and Hospital-Queue (5) and ICU-Queue (7), which allow for the possibility of material delays in admission due to demand exceeding supply. The following section outlines the steps undertaken in order to assess the potential of this model to support public health policy decision-makers.

6. Model Testing and Articulation

The process of postulating the model brought to light a number of issues. Some were formal questions, such as whether and on what basis some of the state-transitions could logically arise. Many others, however, were concerned with the appropriate representation of real-world states and processes.

The COVID-19 pandemic was only just developing; so no directly-relevant case studies were available. One approach would have been to search out case studies of other pandemics, and use those as a basis for assessing the usefulness of the model. However, it was already clear that there were distinct differences between the COVID-19 pandemic and other well-documented events, even other coronavirus events.

A more appropriate method might therefore have been to conduct a contemporaneous field study, seeking embedment within some particular jurisdiction's public health policy apparatus, and preparing a longitudinal case study of that jurisdiction's path, including uncontrolled events, interventions, and subsequent experiences. However, such access would have been very difficult to negotiate (not least in a context in which physical distancing was being imposed). It would also have to a considerable extent limited the testing and articulation process to the factors that arose in that specific jurisdiction. Each country has its own prior context, and the events, the details of the interventions, the sequences of events, and the timings of events, varied greatly among different jurisdictions. It would be very challenging to try to draw generically useful inferences from such a field study.

An alternative approach was accordingly formulated. Monitoring was undertaken of the ongoing reporting of developments, interventions and experiences in countries worldwide. These reports provided a wide range of circumstances against which the efficacy of the model could be reviewed. Two complementary passes were taken across the accumulated information. The first section below reviews the state of knowledge of developments at the end of 2020, 8 months after the model was postulated. The following section then considers particular themes that emerged during that period. Key characteristics of the disease and its impacts were presented earlier; so these analyses emphasise policy interventions. This process stimulated reconsideration of many aspects of the April 2020 version of the model in order to ensure it could assist policy-makers in navigating their jurisdiction's particular maze. The result was a revised model, incorporating many adaptations.

6.1 The State of Knowledge at the End of 2020

Governments around the world responded to COVID-19 with a wide range of interventions intended to protect public health. A scan was undertaken of documents published by relevant international and national government agencies, including WHO (2020d) and ICAO (2020), supplemented by academic articles and media reports. Table 4 identifies mainstream public health interventions, clustered into six groups. It is important that IT be brought to bear to assist policy-makers to judge the likely effectiveness of these actions in particular contexts, to design interventions, and to time and manage their implementation, adaptation and eventual withdrawal.

Table 4: The Primary Public Health Interventions

Case Discovery and Management

•   Identification of suspects

•   Quarantine of suspects

•   Testing of suspects

•   Isolation of infectees

•   Contact-tracing of infectees

•   Location of and communication with contacts

Facility Restrictions and Closedown

•   Hospitals

•   Aged care facilities

•   Institutions, e.g. prisons

•   Group accommodation,

    e.g. backpacker dormitories

•   Face-to-face businesses

    (shops, personal services, gyms)

•   Workplaces

•   Entertainment venues

•   Public gatherings

•   Geographical areas (cordon sanitaire)

•   Pre-schools, schools,

    tertiary educational institutions

Personal Protection

•   Hand hygiene

•   Respiratory etiquette

    (sneeze/cough protection)

•   Avoidance of surfaces

•   Face-masks

•   Clinical Personal Protective Equipment

    (PPE) in hospitals and aged-care facilities

Environmental Measures

•   Cleaning of surfaces

Physical Distancing Requirements

•   Physical distancing in public places

    (1.5m / 4sqm)

•   Quarantining of suspects

•   Isolation of infectees

•   Count-limitations on public gatherings

•   Curfews on entertainment venues

•   'Work-at-Home'

    Recommendations to Employers

•   'Stay-At-Home'

    Recommendations for at-risk segments

Travel-Related Interventions

•   Border restrictions

•   Border screening and testing

•   Border closure

•   Stay-at-home, work-at-home

•   Domestic movement restrictions:

    •   Public transport

    •   Private vehicles

    •   Walking

Because the infection-vector appeared to be primarily brief, airborne transmission from infectees to those close by, physical separation between people (widely referred to using the misleading term 'social distancing') loomed large among the interventions used. The term 'Quarantine' applies to people who have been, or are suspected to have been, exposed to an infectious disease, but who are not at that stage known to be infected. Its use in English for this purpose dates to at least the 17th century. The term 'Isolation', on the other hand, is applied to people known to be infected. It appears to have been used in this manner since the late 19th century. Clear explanations are provided by a range of national health agencies, e.g. in CDC (2020).

In both cases:

A great deal of confusion was evident in the media about both the categories of people to whom the terms 'quarantine' and 'isolation' applied, and the constraints to which they were subject. This derived in part from confused and confusing use of the terms by government health agencies. For example, the NSW government used the term 'quarantine' solely for what was effectively self-funded imprisonment of arrivals from overseas, and applied the term 'isolation' not only to infectees, but also to suspected infectees, close contacts of infectees, and everyone awaiting test-results whether or not they were in any risk-category (NSWG 2020). It appears reasonable to assume that a great many individuals subjected to quarantine and isolation requests and requirements were unclear about what the terms meant and what conditions applied to them.

Countries around the world adopted vastly different approaches to interventions, recognised different triggering events, and timed their interventions differently. Most also changed their approaches over time. Despite the enormous differences in context, some comparisons are feasible, such as among Scandinavian countries. Sweden implemented only limited actions (physical distancing, bans on large gatherings, and travel restrictions), whereas its neighbours used additional and stronger interventions to reduce the opportunity for the virus to spread, including closedown of many more categories of venue, curfews and border closures. The outcome was a death-rate per capita in Sweden 3 times that of its neighbour Denmark, and 10 times that of Norway and Finland (Barrett 2020).

There may also be lessons to be learnt from the juxtaposition of the apparently worst examples of mismanagement and/or outcomes (in particular, the UK, the USA, Belgium) and the most successful (e.g. China, Singapore, New Zealand). The UK flirted with a no-action 'strategy' rationalised as striving for herd immunity, overrode professional advice, lacked coherent and consistent leadership, reacted slowly to new information, and continually changed tack in a haphazard manner (Minghella 2020). All of the most successful countries, on the other hand, acted quickly and decisively.

Belgium's poor performance appears to have derived from a lack of safeguards in aged care facilities, and shortages of protective equipment in facilities generally, compounded by an only marginally functional government, resulting slow response, and a lack of government credibility with the public. There may also have been significant deficiencies in specific policies, in their expression, and in the resulting practices. For example, at the end of 2020, the government's FAQ blurred the concepts of quarantine and isolation, required isolation for only 7 days and without supervision, and contact tracing appears to have not scaled with the rapid growth in cases, and may not have been conducted with the effectiveness achieved in other countries (He et al. 2020, PHB 2020).

A wide range of media reports and some semi-formal reviews gave rise to the list of public health management behaviours associated with serious failure in Table 5.

Table 5: Behaviours Associated with Serious Failure

On the other hand, a number of interventions, and characteristics of interventions, were associated with success in preventing spread and/or reining in spread that had already begun (e.g. OxCGRT 2020). The list of actions in Table 6 was prepared on the basis of reports about actions and outcomes in China (BBC 2020), New Zealand (Baker et al. 2020, Jefferies et al. 2020), Melbourne (Gn 2020a), and Vietnam and Taiwan (Whitworth 2020). The intensity of application was somewhat greater in the countries with more authoritarian regimes, e.g. China's strategy included prompt and massive testing programs in areas where cases emerged, followed by enforced isolation and quarantine, and the responses in Vietnam and Taiwan featured intensive contact-tracing and enforced quarantine, followed by sustained contact management. Several other summary reports were also considered (Jayadevan R. 2020, Firth et al. 2020, Vally 2020).

Effective strategies appeared to have featured a suite of complementary actions that reflected domestic socio-economic conditions. Generally, the strategy needed to encompass case identification, infectee isolation, high-quality contact tracing, suspect quarantine, and healthcare measures. Details of facility closedown orders, physical distancing measures, and travel restrictions, all needed to reflect local realities.

Table 6: Actions Associated with Success

Known-Infectee Control Measures

Community-Spread Control Measures

High-Risk-Segment Protection Measures

Several other interventions may also make significant contributions. For example, some countries applied quarantine requirements to households rather than to individuals, and some imposed quarantine on individuals who had been, even for a relatively short period, in a location close to where an infectee was, or recently had been. A highly visible intervention was requests or requirements to wear masks when in public. The effectiveness of multi-use, commercially-available masks (as distinct from fresh, clinical-grade masks) was in considerable doubt, e.g. SWPRS 2020a). At the end of 2020, the official position of the Australian government remained "There is limited, indirect, experimental evidence that certain types of cloth mask can reduce transmission of respiratory droplets, but they are significantly less efficient than surgical masks" (ADOH 2020). The reinforcement effect of mask-wearing on physical distancing behaviour may have been a more significant beneficial factor than the filtering of outbound and/or inbound droplets.

The previous section summarised insights that had accumulated at the end of 2020. This section considers particular themes within that broad field of view, takes into account the gradual accretion of understanding, and considers the extent to which the original model usefully encompasses and represents elements that came to be recognised as being of importance.

6.2 Particular Themes

The realities of testing individuals for infection raised doubt about representation of testing as a state. Instead, Tested-Awaiting-Result, Tested-Negative (with a counter to allow for multiple tests), and Tested-Positive, may be better specified as important attributes of each individual, travelling with them as they pass through the network. This removes one state and four flows, with no loss of model richness. For ease of reading, the state Quarantined was re-numbered as (2).

A considerable range of challenges arose in the context of detecting infectees. Some of these had to do with the sensitivity of the tests, the false-negative/false-positive balance, and ways to shorten the delay in receiving test-results. Given the impossibility of testing the whole population, even once, let alone repetitively, decisions were needed on what categories of people to prioritise such as close contacts of infectees, less-close contacts, health care workers, the aged, those with compromised immune-systems, children, asymptomatic infectees, and people who had been previously infected. This has implications for the attributes of people that need to be reflected in the model.

In order to balance health safety against social and economic disruption, it was vital to be able to judge the appropriate length of time for quarantine of suspects and isolation of infectees. That depended on the ability to make reasonable determinations about the default period of confinement (requiring an estimate of when infection occurred) and the circumstances under which shorter and longer periods may be appropriate. A guideline for discontinuing transmission-based precautions that was available in mid-2020 was that patients could be released 10 days after symptom onset plus 3-4 days without symptoms, or, in asymptomatic cases, 10 days after a positive test (WHO 2020a). In some countries, that was later adjusted to include a negative test prior to discharge. In order to support analyses, additional attributes needed to be recognised, such as dates and results of tests for the infection, and perhaps also for immunity.

An important aspect of the public health problem is advance warning about the capacity of health facilities to cope with demand. It would be a valuable contribution if the model were able to assist in projecting demand for and supply of hospital-beds and ICU beds, in total, and by geographical area and hospital, based on recent rates of testing, positive cases from testing, hospitalisation-rates of positive cases, the proportion admitted to ICU, and the periods patients spend in those facilities. The source-data and the projection methods would need to be maintained, in order to ensure that current indicators were readily to hand. This draws to attention another important attribute of individuals: non-COVID admissions to hospital queues and onwards.

It became apparent that a number of population segments need to be managed and analysed separately:

The appropriate way to identify these sub-populations is by assigning attributes to each individual to reflect the relevant characteristics. Analyses that incorporate consideration of such segments can assist in refining strategies. For example, some workplaces can be readily virtualised, with only limited impact on the delivery of goods and services, whereas other workplaces inherently require employees to be co-located and even in close proximity with one another. So some industry sectors may require more enforcement action but also more support in order to reduce opposition and mitigate the negative economic impacts. Similarly, if under-10s neither suffer greatly from the virus, nor are significant sources of spread, the closure of child-care services and primary schools may offer limited public health benefit, yet have substantial economic impacts because it removes individuals from the workforce. (For many parents, work - whether on employers' premises or at home - becomes infeasible without child-minding support).

One of the challenging questions was the extent to which all infectees have much the same degree of infectiveness, or whether there are 'super-spreaders' who are much more prone to infecting other people and/or contaminating surfaces. If there is considerable variability, effort could be valuably invested in determining what infectee-attributes are associated with 'super-spreaders' and whether it is possible to focus available tracing and quarantine resources on people with those attributes.

The importance of the category of people in the the state Undetected (64) became apparent as the epidemic unfolded in each country, because undetected infectees may well be a primary source of virus-spread. In order to gain an insight into the overall progress of the epidemic at a population level, an estimate is needed of the Undetected-Infected status, for example by means of adequate random-sample testing of the public for the virus. Strategies are needed to find more of the people who are in that state, so that they can be requested or required to shift state to Isolated (4). Possibilities include extensions to contact-tracing, suspect-definition based on locations and time-periods, infection-testing in the vicinity of hotspots, and random infection-testing. It may be possible to estimate the scale of the count in Undetected (64), by random-testing for antibodies in order to develop estimates of the cumulative count in Undetected-Recovered (88), and to then reason back from there to the scale of current Undetecteds.

To reflect the uncertainties, there are benefits in using ghostly outlines to represent both of the states Undetected (64) and Undetected-Recovered (88), and the inflows to those two states. On the other hand, transitions are visible when an individual moves from Undetected (64) to Detected (3) or Hospital-Queue (5).

The management of arrivals into the jurisdiction appears to be a relatively controllable area of vulnerability, because border-management laws, resources and procedures are already in place. In practice, however, many intra-national borders (even between states and provinces, let alone local regions) are not tightly controlled, and even not legally or practicably controllable; and even some international borders are porous, because of seas, mountain-ranges, large numbers of road-crossings, and treaties requiring ease of passage.

Further challenges that arose included:

The progressive appreciation of these factors makes clear that the original model did not adequately reflect the complexities. Arrivals may have unknown COVID status as the original model assumed, but alternatively they may be landed into Quarantined (2), Detected (3), Isolated (4) or Hospital-Queue (5) or even Detected-Recovered (80). However, all of these may be best represented by specifying Arrival into Population (1) with immediate transition onwards. Further, people in the state Quarantined (2) could not only transition back to Population (1) or forward into Hospital-Queue (5) but also, if tested-positive, convert directly to Isolated (4).

The terminal state Dead (99) needed to be categorised more finely according to cause of death, distinguishing (99A) for those cases where COVID-19 was the cause of death (or a significant factor in the death, by compounding prior conditions) from all other causes of death (99B), including not only where COVID-19 was not present, but also where infection was, or was assumed to be, present at death but it was not listed as a cause. The count of cases that reach state (99A) is likely to represent a large proportion of excess mortality.

One of the many difficulties confronting policy-makers is the near-impossibility of assessment of the impact of particular interventions. Outcomes reflect not only the aftermath of prior interventions, but also the impact of other new interventions undertaken in parallel, interactions among all interventions whose impacts are present at the time, and a host of environmental factors. Examples of possible interventions whose potential needs to be thought through in a variety of contexts are:

6.3 The Revised Model

In order to reflect the insights arising from 8 months of vicarious learning from many different jurisdictions, the April 2020 model in Figure 4 was adapted. The result is in Figure 5.

Figure 5: The Revised Model

The revised model still embodies a great many assumptions. This is because it is intentionally simplified, in order to be understandable by its users. Only those aspects of the real-world system are incorporated as are believed to be the most significant factors affecting policy decisions. For example, an intentional limitation of the model is that it does not cater for individuals who are not infected with COVID-19 but who require hospitalisation and even ICU admission for other reasons. As a result, this version of the model does not support analyses of health-facility capacity-limitations under conditions of very high demand. If that is of importance to the team of policy-makers applying the model, the enhancements required are reasonably modest.

7. Conclusions

IT, despite the great enthusiasm that is shown for its application, delivered relatively little value during the COVID-19 pandemic of 2020. Most uses of IT evidenced an 'applied' approach, effectively 'throwing technology at the problem' and at worst matching the caricature of 'when you have a hammer in your hand, everything looks like a nail'.

It appears more likely that IT can deliver for society and the economy if the approach adopted is both more strategic in nature, and 'instrumentalist' / problem-oriented rather than 'applied' / tool-oriented. That means standing far enough back to be able to identify and describe the problem-space, and then modelling the key aspects of that space. By building on that foundation, it is much more likely that architectures, process models and data models will emerge and be refined, that will much better serve the needs of decision-makers.

The scale of a pandemic management program, even within a single jurisdiction, is so great that a detailed assessment of the models used during 2020 is difficult to assemble. The research reported in this paper comprised a mixture of thought-experiment, abstract design, and testing and adaptation of the initial model against information arising from experience across the world during the period May to December 2020.

A model was initially postulated that was envisaged as being suitable as a supporting tool for public policy decision-making in public health management. On the basis of new information streaming in during the subsequent months, the need for refinements was apparent. The revised model is capable of further articulation, through detailed specification of data models in support of states and flows, and alternative processing rules for state transitions, appropriate to any particular jurisdiction.

The model proposed and tested in this paper has, by its nature, limited focus. Its target-area is expressly public health management, although it has application also to the adjacent level of health facility management, and implications for population management. It is not suggested that this model subsumes or replaces models at other levels of abstraction. It is contended, however, that public health policy-makers, political advisers and Ministers can greatly benefit from the development, articulation and ongoing adaptation of a model of this nature. It enables processes to be better understood, strategies considered, and implications of possible interventions thought through. This information can then be combined with that arising from work at other levels of abstraction (bio-medical and medical, on the one hand, and economic management on the other).

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This chapter is a revised version of a Working Paper written between April and December 2020. Parts of the Working Paper have been adapted into a conference paper and a journal article. Feedback from colleagues has assisted in the development of this Chapter, in particular from Profs. Dubravka Cecez-Kecmanovic, Graham Greenleaf, Jürgen Seitz, Robin Stanton, Doug Vogel and Nilmini Wickramasinghe.

Author Affiliations

Roger Clarke is Principal of Xamax Consultancy Pty Ltd, Canberra. He is also a Visiting Professor associated with the Allens Hub for Technology, Law and Innovation in UNSW Law., and a Visiting Professor in the Research School of Computer Science at the Australian National University.

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