An introduction to compartmental modeling for the budding infectious disease modeler

Blackwood, Julie C.; Childs, Lauren M.

doi:10.1080/23737867.2018.1509026

Cited by 112 publications

(152 citation statements)

References 34 publications

(34 reference statements)

Supporting

Mentioning

146

Contrasting

Unclassified

Order By: Relevance

“…Epidemiologically, infectious diseases can be studied mathematically using compartmental models. 6 The SEIR model divides the population into Susceptible, Exposed, Infectious, and Recovered compartments (Fig. 1).…”

Section: Methodsmentioning

confidence: 99%

Healthcare impact of COVID-19 epidemic in India: A stochastic mathematical model

Chatterjee

Kumar

Shankar

2020

Medical Journal Armed Forces India

350

292

View full text Add to dashboard Cite

Available online xxxKeywords: COVID-19 Models Theoretical Quarantine Coronavirus SARS-CoV-2 Epidemiology a b s t r a c t Background: In India, the SARS-CoV-2 COVID-19 epidemic has grown to 1251 cases and 32 deaths as on 30 Mar 2020. The healthcare impact of the epidemic in India was studied using a stochastic mathematical model. Methods: A compartmental SEIR model was developed, in which the flow of individuals through compartments is modeled using a set of differential equations. Different scenarios were modeled with 1000 runs of Monte Carlo simulation each using MATLAB. Hospitalization, intensive care unit (ICU) requirements, and deaths were modeled on SimVoi software. The impact of nonpharmacological interventions (NPIs) including social distancing and lockdown on checking the epidemic was estimated.Results: Uninterrupted epidemic in India would have resulted in more than 364 million cases and 1.56 million deaths with peak by mid-July. As per the model, at current growth rate of 1.15, India is likely to reach approximately 3 million cases by 25 May, implying 125,455 (±18,034) hospitalizations, 26,130 (±3298) ICU admissions, and 13,447 (±1819) deaths. This would overwhelm India's healthcare system. The model shows that with immediate institution of NPIs, the epidemic might still be checked by mid-April 2020. It would then result in 241,974 (±33,735) total infections, 10,214 (±1649) hospitalizations, 2121 (±334) ICU admissions, and 1081 (±169) deaths.Conclusion: At the current growth rate of epidemic, India's healthcare resources will be overwhelmed by the end of May. With the immediate institution of NPIs, total cases, hospitalizations, ICU requirements, and deaths can be reduced by almost 90%.

show abstract

Section: Methodsmentioning

confidence: 99%

Healthcare impact of COVID-19 epidemic in India: A stochastic mathematical model

Chatterjee

Kumar

Shankar

2020

Medical Journal Armed Forces India

350

292

View full text Add to dashboard Cite

show abstract

“…To identify the transmission investment that maximizes the recruitment of infectious mosquitoes from a single infected host, we initially assume no transmission from vector to host (

β_{v h} = 0

), meaning that no mosquitoes can be exposed once initially infected hosts recover (

t > a_{c}

R_{V_{E}} false(t false) = \{\begin{matrix} left V_{S} (t) \frac{b}{N} H_{I} (0) e^{- ψ t} β_{h v} (t), & left when t \leq a_{c} \\ left 0, & left t > a_{c}, \end{matrix}

where

H_{I} false(0 false) e^{- ψ t}

is the number of initially infected hosts not yet recovered by time t and

β_{h v} false(t false)

indicates the probability that a vector contracts the infection upon feeding. The per‐vector biting rate is b divided by the total number of humans ( N , set to 2000), as is conventionally assumed in models of vector transmission (reviewed in Blackwood and Childs ). The biting rate for mosquitoes was assumed to be 0.35 per day, corresponding to the temperature‐dependent curve fit by Mordecai et al.…”

Section: Methodsmentioning

confidence: 99%

“…where H I (0)e −ψt is the number of initially infected hosts not yet recovered by time t and β hv (t) indicates the probability that a vector contracts the infection upon feeding. The per-vector biting rate is b divided by the total number of humans (N , set to 2000), as is conventionally assumed in models of vector transmission (reviewed in Blackwood and Childs 2018). The biting rate for mosquitoes was assumed to be 0.35 per day, corresponding to the temperature-dependent curve fit by Mordecai et al (2013) evaluated at 28 • C. Exposed vectors are recruited into the infectious class according to…”

Section: Figure 1 Cross-scale Model Schematic (A) Transmission Invementioning

confidence: 99%

Partitioning the influence of ecology across scales on parasite evolution

2019

View full text Add to dashboard Cite

Vector‐borne parasites must succeed at three scales to persist: they must proliferate within a host, establish in vectors, and transmit back to hosts. Ecology outside the host undergoes dramatic seasonal and human‐induced changes, but predicting parasite evolutionary responses requires integrating their success across scales. We develop a novel, data‐driven model to titrate the evolutionary impact of ecology at multiple scales on human malaria parasites. We investigate how parasites invest in transmission versus proliferation, a life‐history trait that influences disease severity and spread. We find that transmission investment controls the pattern of host infectiousness over the course of infection: a trade‐off emerges between early and late infectiousness, and the optimal resolution of that trade‐off depends on ecology outside the host. An expanding epidemic favors rapid proliferation, and can overwhelm the evolutionary influence of host recovery rates and mosquito population dynamics. If transmission investment and recovery rate are positively correlated, then ecology outside the host imposes potent selection for aggressive parasite proliferation at the expense of transmission. Any association between transmission investment and recovery represents a key unknown, one that is likely to influence whether the evolutionary consequences of interventions are beneficial or costly for human health.

show abstract

“…Epidemiological Modelling of infectious diseases is dominated by compartmental models which simulate the transition of individuals between various stages of disease [7,8]. We now introduce the Susceptible-Infected-Recovered (SIR) compartmental model that has been dominant in COVID-19 modelling literature [4,5].…”

Section: Epidemiological Modellingmentioning

confidence: 99%

On Data-Driven Management of the COVID-19 Outbreak in South Africa

Mbuvha

Marwala

2020

Preprint

View full text Add to dashboard Cite

The rapid spread of the novel coronavirus (SARS-CoV-2) has highlighted the need for the development of rapid mitigating responses under conditions of extreme uncertainty. While numerous works have provided projections of the progression of the pandemic, very little work has been focused on its progression in Africa and South Africa, in particular. In this work, we calibrate the susceptible-infectedrecovered (SIR) compartmental model to South African data using initial conditions inferred from progression in Hubei, China and Lombardy, Italy. The results suggest two plausible hypotheses -either the COVID-19 pandemic is still at very early stages of progression in South Africa or a combination of prompt mitigating measures, demographics and social factors have resulted in a slowdown in its spread and severity. We further propose pandemic monitoring and health system capacity metrics for assisting decision-makers in evaluating which of the two hypotheses is most probable.

show abstract

An introduction to compartmental modeling for the budding infectious disease modeler

Cited by 112 publications

References 34 publications

Healthcare impact of COVID-19 epidemic in India: A stochastic mathematical model

Healthcare impact of COVID-19 epidemic in India: A stochastic mathematical model

Partitioning the influence of ecology across scales on parasite evolution

On Data-Driven Management of the COVID-19 Outbreak in South Africa

Contact Info

Product

Resources

About