Journal of Public Health International

Journal of Public Health International

Current Issue Volume No: 3 Issue No: 2

Methodology-article Article Open Access
  • Available online freely Peer Reviewed
  • Controlling The Covid-19 Pandemic Without Killing The Economy: About Data Driven Decision Making With A Data Model Assessing Local Transmission Risk

    Stapff Manfred 1
       

    1 LG Chem Life Sciences Innovation Center, Cambridge, MA, USA 

    Abstract

    Context

    In the face of further waves of the COVID-19 pandemic it becomes essential to find a balance between protective actions to guard public health and restrictive measures which can collapse our economy.

    Background

    As a basis for public health decisions, officials still rely on metrics which were helpful in the beginning of the pandemic but are now not precise enough for a focused and targeted approach to keep the spread of the infection under control. This can lead to public mistrust, pandemic tiredness , and can cause unnecessary damage to the economy without having the desired protective effect on public health.

    Methods

    This article discusses various metrics, their advantages and caveats, and it provides suggestions for use in a more targeted and risk-based approach, as an alternative to the current general lock-down practice. It suggests the notion of including a concept of risk contacts per area to better describe the possibility of virus transmission than currently published metrics do. The article also suggests specific analyses of real-world data for identification of populations at risk for severe courses of COVID-19 to allow more targeted protective actions.

    Discussion

    Data currently used to describe the COVID-19 pandemic lack important parameters like population density and local likelihood of potentially infectious contacts. The currently often used all or nothing approach of shut-down orders needs to be replaced by more sophisticated tactics considering individual local exposure risks and need to be balanced towards metrics on economic short term and long-term impact. In addition, smart analyses of real-world data may contribute to effective protection of individuals at risk.

    Author Contributions
    Received Nov 12, 2020     Accepted Nov 14, 2020     Published Nov 20, 2020

    Copyright© 2020 Stapff Manfred.
    License
    Creative Commons License   This work is licensed under a Creative Commons Attribution 4.0 International License. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

    Competing interests

    The authors have declared that no competing interests exist.

    Funding Interests:

    Citation:

    Stapff Manfred (2020) Controlling The Covid-19 Pandemic Without Killing The Economy: About Data Driven Decision Making With A Data Model Assessing Local Transmission Risk Journal of Public Health International. - 3(2):22-29
    DOI 10.14302/issn.2641-4538.jphi-20-3621
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