Quantitative Analysis of the Effectiveness of Public Health Measures on COVID-19 Transmission

  1. Thiago Christiano Silva
  2. Leandro Anghinoni
  3. Liang Zhao

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Abstract

Although COVID-19 has spread almost all over the world, social isolation is still a controversial public health policy and governments of many countries still doubt its level of effectiveness. This situation can create deadlocks in places where there is a discrepancy among municipal, state and federal policies. The exponential increase of the number of infectious people and deaths in the last days shows that the COVID-19 epidemics is still at its early stage in Brazil and such political disarray can lead to very serious results. In this work, we study the COVID-19 epidemics in Brazilian cities using early-time approximations of the SIR model in networks. Different from other works, the underlying network is constructed by feeding real-world data on local COVID-19 cases reported by Brazilian cities to a regularized vector autoregressive model, which estimates directional COVID-19 transmission channels (links) of every pair of cities (vertices) using spectral network analysis. Our results reveal that social isolation and, especially, the use of masks can effectively reduce the transmission rate of COVID-19 in Brazil. We also build counterfactual scenarios to measure the human impact of these public health measures in terms of reducing the number of COVID-19 cases at the epidemics peak. We find that the efficiency of social isolation and of using of masks differs significantly across cities. For instance, we find that they would potentially decrease the COVID-19 epidemics peak in Sao Paulo (SP) and Brasilia (DF) by 15% and 25%, respectively. We hope our study can support the design of further public health measures.

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    SciScore for 10.1101/2020.05.15.20102988: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

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  2. Version published to 10.1101/2020.05.15.20102988v1 on medRxiv