Prudent public health intervention strategies to control the coronavirus disease 2019 transmission in India: A mathematical model-based approach

Abstract

Background & objectives: Coronavirus disease 2019 (COVID-19) has raised urgent questions about containment and mitigation, particularly in countries where the virus has not yet established human-to-human transmission. The objectives of this study were to find out if it was possible to prevent, or delay, the local outbreaks of COVID-19 through restrictions on travel from abroad and if the virus has already established in-country transmission, to what extent would its impact be mitigated through quarantine of symptomatic patients?

Methods: These questions were addressed in the context of India, using simple mathematical models of infectious disease transmission. While there remained important uncertainties in the natural history of COVID-19, using hypothetical epidemic curves, some key findings were illustrated that appeared insensitive to model assumptions, as well as highlighting critical data gaps.

Results: It was assumed that symptomatic quarantine would identify and quarantine 50 per cent of symptomatic individuals within three days of developing symptoms. In an optimistic scenario of the basic reproduction number (R₀) being 1.5, and asymptomatic infections lacking any infectiousness, such measures would reduce the cumulative incidence by 62 per cent. In the pessimistic scenario of R₀=4, and asymptomatic infections being half as infectious as symptomatic, this projected impact falls to two per cent.

Interpretation & conclusions: Port-of-entry-based entry screening of travellers with suggestive clinical features and from COVID-19-affected countries, would achieve modest delays in the introduction of the virus into the community. Acting alone, however, such measures would be insufficient to delay the outbreak by weeks or longer. Once the virus establishes transmission within the community, quarantine of symptomatics may have a meaningful impact on disease burden. Model projections are subject to substantial uncertainty and can be further refined as more is understood about the natural history of infection of this novel virus. As a public health measure, health system and community preparedness would be critical to control any impending spread of COVID-19 in the country.

Keywords: COVID-19; deterministic model; mathematical model; mitigation; quarantine; Airport screening; transmission.

Fig. 1

Summary of the model structure used to represent coronavirus disease 2019 transmission and control in Indian cities. The population in each metropolitan area is divided into different compartments, representing states of disease, with flows between compartments given by the rates shown in the diagram. Thus, susceptible individuals (S), upon acquiring infection, enter a state of asymptomatic infection (E) and with some delay develop symptomatic disease (I). It is assumed that a proportion p of symptomatic cases is subject to quarantine [I^(q)] and the remainder [I⁽ⁿ⁾] is not. The relative size of these two populations (p) reflects the coverage of quarantine efforts. Individuals in I^(q) are quarantined with an average quarantine delay (1/δ). Finally, individuals may be cured (R) or die as per recovery rate (γ) or mortality rate (μ), respectively. Those people who are successfully quarantined (Q) do not contribute to onward infection.

Fig. 2

Model projections for the time to epidemic in India (the time to reach a prevalence of 1000 cases), under different scenarios for the intensity of port-of-entry screening. The left half of the figure illustrates the effect, on epidemic timing, of screening symptomatic passengers alone; the right half illustrates the additional effect of diagnosing coronavirus disease-19 amongst asymptomatic passengers, assuming full screening of symptomatic passengers (infeasible, but illustrative). Solid lines show central estimates, whereas dashed lines span 95 per cent of simulated uncertainty intervals.

Fig. 3

Model projections for the hypothetical epidemic dynamics (symptomatic prevalence over time) with and without intervention under different scenarios for epidemiologic parameters considering an intervention, in which 50 per cent of the symptomatic cases are isolated within three days of developing symptoms.

Fig. 4

Model projections for the per cent reduction in hypothetical peak prevalence and per cent reduction in hypothetical cumulative incidence by initiation of quarantine of symptomatics within two, three and four days under the 'optimistic' (A) and 'pessimistic' (B) scenarios described in the main text.

Fig. 5

Projected duration of epidemic (days) for the scenarios with and without symptomatic quarantine at 50 per cent coverage in three days by R₀ and relative infectiousness of asymptomatic cases. Here, the 'epidemic duration' is measured as the duration (in days) over which the prevalence of symptomatic infection is >1 case.