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

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.


SA: Notes on Parameters
In this section we further explain how some of our parameters were obtained.

Duration per contact (τ):
Duration per contact (τ i ) for an age-group i is given by the reciprocal of the total number of contacts per day (m i ) for that age group.
The duration per contact between susceptible individuals in age-group i with infectious individuals in age-group j, denoted by τ ij , is the same as τ i because for any given age-group i, all contacts across age-groups j are assumed to be of the same duration. [2] Nepal's age-distribution across all 5-year age-groups is also included in the table below:

Transmissions per day (η):
Transmissions per day (η) is simply calculated as the ratio of the Reproduction number (R 0 ) and the infectious duration (1/γ). Since Ro is sampled from a uniform distribution (2·0--2·8) and infectious duration is 7 days, the values of η range between 2/7 = 0.29 per day to 2.8/7 = 0.4 per day.

Transmission rate ( ) β
Based on the above values, the age-specific transmission rate is then calculated as follows (Equations (i) and (ii) in the main text):

Excess mortality factor (θ)
Calculation of the excess mortality factor is explained in Section SC:

General ward and ICU mortality ratios
The general ward and ICU mortality ratios are calculated by dividing the age-specific mortality ratios by the hospitalization rate and multiplying by the ICU death factor (f = 0.8) for ICU mortality ratio and (1-f) for the General ward mortality ratio. We assume that all deaths happen among patients who require hospitalization. Nepal's age-distribution across all 5-year age-groups is also included in the table below:

SB: Estimates of Intervention Effectiveness
Here we present detailed estimates for the effectiveness of interventions that we  The difference in access to health services is likely to result in a significant difference in mortality rates between China and Kathmandu. We accounted for this difference in the following way. Until patient volumes surpass capacity, we assume that age-specific mortality rates will remain the same as they were in China. On any day that demands exceed bed capacity, we assume that mortality rate for patients unable to find hospital beds increases proportionate to the relative deficit of per-capita hospital beds in Kathmandu as compared to China. We calculated this relative deficit by subtracting the bed ratio in Kathmandu from the ratio in China and dividing it by the bed ratio in Kathmandu. An early analysis from China has found that mortality correlates with epidemic burden beyond health service capacity. [5] We then calculated excess deaths due to the lack of healthcare.
Two other factors are likely to contribute to a difference in mortality rates: age structure and comorbidities. We adjusted the mortality ratio to the first factor. Given comparable rates of risk factors like obesity and smoking between China and urban Nepal, we assume they have comparable comorbidity burden. [6][7][8] With (excess mortality factor) would lead to mortality to increase by 10 100 to 20 500, as compared to the base scenario. If total bed capacity were increased by a 1000, this excess mortality would fall by 32% and by 49% if capacity could be increased by 2 000 beds. If a capacity increase of 1 000 beds could be combined with a month long lockdown along with year-long physical distancing measures the excess mortality due to the deficit of healthcare would fall by 89%.