Abstract
Non-pharmaceutical interventions (NPIs) including resource allocation, risk communication, social distancing and travel restriction, are mainstream actions to control the spreading of Coronavirus disease 2019 (COVID-19) worldwide. Different countries implemented their own combinations of NPIs to prevent local epidemics and healthcare system overloaded. Portfolios, as temporal sets of NPIs have various systemic impacts on preventing cases in populations. Here, we developed a probabilistic modeling framework to evaluate the effectiveness of NPI portfolios at the macroscale. We employed a deconvolution method to back-calculate incidence of infections and estimate the effective reproduction number by using the package EpiEstim. We then evaluated the effectiveness of NPIs using ratios of the reproduction numbers and considered them individually and as a portfolio systemically. Based on estimates from Japan, we estimated time delays of symptomatic-to-confirmation and infection-to-confirmation as 7.4 and 11.4 days, respectively. These were used to correct surveillance data of other countries. Considering 50 countries, risk communication and returning to normal life were the most and least effective yielding the aggregated effectiveness of 0.11 and − 0.05 that correspond to a 22.4% and 12.2% reduction and increase in case growth. The latter is quantified by the change in reproduction number before and after intervention implementation. Countries with the optimal NPI portfolio are along an empirical Pareto frontier where mean and variance of effectiveness are maximized and minimized independently of incidence levels. Results indicate that implemented interventions, regardless of NPI portfolios, had distinct incidence reductions and a clear timing effect on infection dynamics measured by sequences of reproduction numbers. Overall, the successful suppression of the epidemic cannot work without the non-linear effect of NPI portfolios whose effectiveness optimality may relate to country-specific socio-environmental factors.
Highlights
Non-pharmaceutical interventions (NPIs) including resource allocation, risk communication, social distancing and travel restriction, are mainstream actions to control the spreading of Coronavirus disease 2019 (COVID-19) worldwide
The proposed probabilistic model demonstrates the systemic impact of intervention portfolios from which distinct NPI effectiveness is inferred without a-priori assumption on NPI interdependencies
The smooth infection curve is the theoretical expectation about epidemic dynamics without extra stochastic forcing
Summary
Non-pharmaceutical interventions (NPIs) including resource allocation, risk communication, social distancing and travel restriction, are mainstream actions to control the spreading of Coronavirus disease 2019 (COVID-19) worldwide. Some evaluation tools have been proposed recently, such as the Global COVID Index[13] and the Government Response Stringency Index[14], but these tools focus on ranking countries or measuring the amount of interventions rather than dissecting and quantifying vulnerabilities and interventions precisely These indices rely on information of the pandemic as it is without embracing a probabilistic approach and questioning wether systematic uncertainties to correct are present. According to various social and political conditions, each country has taken different schemes of NPIs involving different intervention options considering type, timing, duration and hypothesized s trength[20,21] These interventions have been driven by a reaction approach in which most countries were reacting to increasing cases by implementing what other countries implemented to restrict individual behavior leading to infection. The RL model was slightly modified for a better treatment of noise and the easy incorporation of available non-linear a priori information[27]
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