Best practice assessment of disease modelling for infectious disease outbreaks

Dembek, Zygmunt F.; Chekol, Tesema; Wu, Aiguo

doi:10.1017/s095026881800119x

Cited by 9 publications

(5 citation statements)

References 93 publications

(122 reference statements)

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“…We investigate the effect of a 6-week intervention during which the effective contact rate

is reduced from 3 to 1.6. Such interventions in an influenza epidemic can delay the growth of the epidemic [ 17 ]. Our simulations show this delay for an early intervention.…”

Section: Resultsmentioning

confidence: 99%

When the Best Pandemic Models are the Simplest

Jahedi

Yorke

2020

Biology

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As the coronavirus pandemic spreads across the globe, people are debating policies to mitigate its severity. Many complex, highly detailed models have been developed to help policy setters make better decisions. However, the basis of these models is unlikely to be understood by non-experts. We describe the advantages of simple models for COVID-19. We say a model is “simple” if its only parameter is the rate of contact between people in the population. This contact rate can vary over time, depending on choices by policy setters. Such models can be understood by a broad audience, and thus can be helpful in explaining the policy decisions to the public. They can be used to evaluate the outcomes of different policies. However, simple models have a disadvantage when dealing with inhomogeneous populations. To augment the power of a simple model to evaluate complicated situations, we add what we call “satellite” equations that do not change the original model. For example, with the help of a satellite equation, one could know what his/her chance is of remaining uninfected through the end of an epidemic. Satellite equations can model the effects of the epidemic on high-risk individuals, death rates, and nursing homes and other isolated populations. To compare simple models with complex models, we introduce our “slightly complex” Model J. We find the conclusions of simple and complex models can be quite similar. However, for each added complexity, a modeler may have to choose additional parameter values describing who will infect whom under what conditions, choices for which there is often little rationale but that can have big impacts on predictions. Our simulations suggest that the added complexity offers little predictive advantage.

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“…We investigate the effect of a 6-week intervention during which the effective contact rate

is reduced from 3 to 1.6. Such interventions in an influenza epidemic can delay the growth of the epidemic [ 17 ]. Our simulations show this delay for an early intervention.…”

Section: Resultsmentioning

confidence: 99%

When the Best Pandemic Models are the Simplest

Jahedi

Yorke

2020

Biology

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“…The semi-logarithmic plot of discovered infections in Serbia is shown in Figure 2(b). This is the usual behavior of many models of infective illness epidemic outbreaks [4]. After a small number in the first several day number of cases grows from 5 to 72 within 7 days from Mar.…”

Section: Data Description and Importancementioning

confidence: 70%

Epidemiological control measures and predicted number of infections for SARS-CoV-2 pandemic: case study Serbia march–april 2020

Djurović¹

2020

Heliyon

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Background: In this paper, we are studying the response of the Serbian government and health authorities to the SARS-CoV-2 pandemic in the early stage of the local outbreak between Mar. 15 th and Apr. 15 th , 2020 by predictive numerical models. Such a study should be helpful to access the effectiveness of measures conducted to suppress the pandemic at a local scale. Methods: We have performed extrapolation of the number of SARS-CoV-2 infections with the first stable set of data exploiting exponential growth (linear in logarithmic scale). Based on obtained coefficients it is performed prediction of a number of cases until the end of March. After initial exponential growth, we have changed predictive model to the generalized gamma function. Obtained results are compared with the number of infections and the prediction for the remainder of the outbreak is given. Findings: We have found that the daily growth rate was above 21.5% at the beginning of the period, increased slightly after the introduction of the State of Emergency and the first set of strict epidemical control measures. It took about 13 days after the first set of strict measures to smooth daily growth. It seems that early government measures had an only moderate impact to reduce growth due to the social behavior of citizens and influx of diaspora returning to Serbia from highly affected areas, i.e., the exponential growth of infected persons is kept but with a reduced slope of about 14-15%. Anyway, it is demonstrated that period required that any measure has effect is up to 15 days after introduction, firstly to exponential growth with a smaller rate and after to smooth function representing the number of infected persons below exponential growth rate. Conclusions: Obtained results are consistent with findings from other countries, i.e., initial exponential growth slows down within the presumed incubation period of 2 weeks after adopting lockdown and other nonpharmaceutical epidemiological measures. However, it is also shown that the exponential growth can continue after this period with a smaller slope. Therefore, quarantine and other social distancing measures should be adopted as soon as possible in a case of any similar outbreak since alternatives mean prolonged epidemical situation and growing costs in human life, pressure on the health system, economy, etc. For modeling the remainder of the outbreak generalized gamma function is used showing accurate results but requiring more samples and preprocessing (data filtering) concerning exponential part of the outbreak. We have estimated the number of infected persons for the remaining part of the outbreak until the end of June.

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“…We investigate the effect of a 6-week intervention during which the effective contact rate β is reduced from 3 to 1.6. Such interventions in an Influenza epidemic can delay the growth of the epidemic [16]. Our simulations show this delay for an early intervention.…”

Section: Resultsmentioning

confidence: 88%

When the best pandemic models are the simplest

Jahedi

Yorke

2020

Preprint

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As a pandemic of coronavirus spreads across the globe, people debate policies to mitigate its severity. Many complex, highly detailed models have been developed to help policy setters make better decisions. However, the basis of these models is unlikely to be understood by non-experts. We describe the advantages of simple models for covid-19. We say a model is simple if its only parameter is the rate of contact between people in the population. This contact rate can vary over time, depending on choices by policy setters. Such models can be understood by a broad audience, and thus can be helpful in explaining the policy decisions to the public. They can be used to evaluate the outcomes of different policy strategies. However, simple models have a disadvantage when dealing with inhomogeneous populations.To augment the power of a simple model to evaluate complicated situations, we add what we call satellite equations that do not change the original model. For example, with the help of a satellite equation, one could know what his/her chance is of remaining uninfected through the end of epidemic. Satellite equations can model the effect of the epidemic on high-risk individuals, or death rates, or on nursing homes, and other isolated populations. To compare simple models with complex models, we introduce our slightly complex Model J. We find the conclusions of simple and complex models can be quite similar. But, for each added complexity, a modeler may have to choose additional parameter values describing who will infect whom under what conditions, choices for which there is often little rationale but that can have a big impact on predictions. Our simulations suggest that the added complexity offers little predictive advantage.

show abstract

Best practice assessment of disease modelling for infectious disease outbreaks

Cited by 9 publications

References 93 publications

When the Best Pandemic Models are the Simplest

When the Best Pandemic Models are the Simplest

Epidemiological control measures and predicted number of infections for SARS-CoV-2 pandemic: case study Serbia march–april 2020

When the best pandemic models are the simplest

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