COVID-19 control strategies and intervention effects in resource limited settings: A modeling study

Abstract

Introduction: Many countries with weaker health systems are struggling to put together a coherent strategy against the COVID-19 epidemic. We explored COVID-19 control strategies that could offer the greatest benefit in resource limited settings.

Methods: Using an age-structured SEIR model, we explored the effects of COVID-19 control interventions-a lockdown, physical distancing measures, and active case finding (testing and isolation, contact tracing and quarantine)-implemented individually and in combination to control a hypothetical COVID-19 epidemic in Kathmandu (population 2.6 million), Nepal.

Results: A month-long lockdown will delay peak demand for hospital beds by 36 days, as compared to a base scenario of no intervention (peak demand at 108 days (IQR 97-119); a 2 month long lockdown will delay it by 74 days, without any difference in annual mortality, or healthcare demand volume. Year-long physical distancing measures will reduce peak demand to 36% (IQR 23%-46%) and annual morality to 67% (IQR 48%-77%) of base scenario. Following a month long lockdown with ongoing physical distancing measures and an active case finding intervention that detects 5% of the daily infection burden could reduce projected morality and peak demand by more than 99%.

Conclusion: Limited resource settings are best served by a combination of early and aggressive case finding with ongoing physical distancing measures to control the COVID-19 epidemic. A lockdown may be helpful until combination interventions can be put in place but is unlikely to reduce annual mortality or healthcare demand.

Fig 1. Age and intervention specific contact matrices for Nepal.

In these matrices, the population is divided into 16 5-year age-groups represented along the two axes that start at the top left. The top left square represents contacts between 0–4 year olds and the bottom right square represents contacts between individuals who are 75 years and older. Colors in the squares represent the log transformed mean daily contact rate between corresponding 5-year age-groups cohorts. Dark/red colors represent fewer contacts, light/yellow colors represent a greater number of contacts. Physical distancing causes the lighter squares to darken indicating fewer contacts, while a lockdown causes the lighter squares to darken even further.

Fig 2. Flow diagram of the SEIR model for COVID-19 transmission dynamics.

Boxes represent disease state compartments for Susceptible (S), Exposed (E), Infectious (I), and Removed (R), with additional compartments for Quarantine (Q), Isolation (J), Hospitalized in the general ward (H), Hospitalized in the ICU (U) and Dead (D). Arrows represent flow between compartments with the flow determined by their corresponding parameter values. Parameter values are further explained in Table 1.

Fig 3. Timeline for the implementation of time-dependent interventions against COVID-19.

The y-axis labels represent various interventions that were considered. The colour coded horizontal bars represent the duration the respective intervention is in place. The area on the top shaded in blue represents a combination of interventions. Interventions begin with a lockdown that began on March 24. A one year time duration beginning March 24, 2020 has been considered in this study.

Fig 4. A comparison of the effectiveness of interventions against COVID-19 in reducing the demand for hospital beds.

Panel (A) on the top compares lockdown or physical distancing measures implemented for a variable duration as compared to the base scenario of no intervention. Panel (B) in the middle compares active case finding measures with the base scenario. Panel (C) at the bottom compares the effectiveness of a combination of interventions with the base scenario. The blue line represents the health system capacity which is a total of 5400 hospital beds for Kathmandu, including approximately 250 ICU beds. The y-axis has been truncated in Panel (C) to accommodate observations that are closer to the x-axis.

Fig 5. A comparison of the effectiveness of interventions against COVID-19 in reducing mortality burden.

Panel (A) on the top compares lockdown or physical distancing measures implemented for a variable duration as compared to the base scenario of no intervention. Panel (B) in the middle compares active case finding measures with the base scenario. Panel (C) at the bottom compares the effectiveness of a combination of interventions with the base scenario of no intervention. The y-axis has been truncated in Panel (C) to accommodate observations that are closer to the x-axis.