tetano
Editor, Senior Moderator
Eur J Epidemiol
. 2020 May 19.
doi: 10.1007/s10654-020-00649-w. Online ahead of print.
Dynamic Interventions to Control COVID-19 Pandemic: A Multivariate Prediction Modelling Study Comparing 16 Worldwide Countries
Rajiv Chowdhury[SUP] 1 [/SUP], Kevin Heng[SUP] 2 3 [/SUP], Md Shajedur Rahman Shawon[SUP] 4 [/SUP], Gabriel Goh[SUP] 5 [/SUP], Daisy Okonofua[SUP] 6 [/SUP], Carolina Ochoa-Rosales[SUP] 7 8 [/SUP], Valentina Gonzalez-Jaramillo[SUP] 9 [/SUP], Abbas Bhuiya[SUP] 10 [/SUP], Daniel Reidpath[SUP] 11 [/SUP], Shamini Prathapan[SUP] 12 [/SUP], Sara Shahzad[SUP] 6 [/SUP], Christian L Althaus[SUP] 9 [/SUP], Nathalia Gonzalez-Jaramillo[SUP] 9 [/SUP], Oscar H Franco[SUP] 13 [/SUP], Global Dynamic Interventions Strategies for COVID-19 Collaborative Group
Affiliations
Abstract
To date, non-pharmacological interventions (NPI) have been the mainstay for controlling the coronavirus disease-2019 (COVID-19) pandemic. While NPIs are effective in preventing health systems overload, these long-term measures are likely to have significant adverse economic consequences. Therefore, many countries are currently considering to lift the NPIs-increasing the likelihood of disease resurgence. In this regard, dynamic NPIs, with intervals of relaxed social distancing, may provide a more suitable alternative. However, the ideal frequency and duration of intermittent NPIs, and the ideal "break" when interventions can be temporarily relaxed, remain uncertain, especially in resource-poor settings. We employed a multivariate prediction model, based on up-to-date transmission and clinical parameters, to simulate outbreak trajectories in 16 countries, from diverse regions and economic categories. In each country, we then modelled the impacts on intensive care unit (ICU) admissions and deaths over an 18-month period for following scenarios: (1) no intervention, (2) consecutive cycles of mitigation measures followed by a relaxation period, and (3) consecutive cycles of suppression measures followed by a relaxation period. We defined these dynamic interventions based on reduction of the mean reproduction number during each cycle, assuming a basic reproduction number (R[SUB]0[/SUB]) of 2.2 for no intervention, and subsequent effective reproduction numbers (R) of 0.8 and 0.5 for illustrative dynamic mitigation and suppression interventions, respectively. We found that dynamic cycles of 50-day mitigation followed by a 30-day relaxation reduced transmission, however, were unsuccessful in lowering ICU hospitalizations below manageable limits. By contrast, dynamic cycles of 50-day suppression followed by a 30-day relaxation kept the ICU demands below the national capacities. Additionally, we estimated that a significant number of new infections and deaths, especially in resource-poor countries, would be averted if these dynamic suppression measures were kept in place over an 18-month period. This multi-country analysis demonstrates that intermittent reductions of R below 1 through a potential combination of suppression interventions and relaxation can be an effective strategy for COVID-19 pandemic control. Such a "schedule" of social distancing might be particularly relevant to low-income countries, where a single, prolonged suppression intervention is unsustainable. Efficient implementation of dynamic suppression interventions, therefore, confers a pragmatic option to: (1) prevent critical care overload and deaths, (2) gain time to develop preventive and clinical measures, and (3) reduce economic hardship globally.
Keywords: COVID-19; Dynamic interventions; Epidemiology; Infectious disease; Prediction modelling.
. 2020 May 19.
doi: 10.1007/s10654-020-00649-w. Online ahead of print.
Dynamic Interventions to Control COVID-19 Pandemic: A Multivariate Prediction Modelling Study Comparing 16 Worldwide Countries
Rajiv Chowdhury[SUP] 1 [/SUP], Kevin Heng[SUP] 2 3 [/SUP], Md Shajedur Rahman Shawon[SUP] 4 [/SUP], Gabriel Goh[SUP] 5 [/SUP], Daisy Okonofua[SUP] 6 [/SUP], Carolina Ochoa-Rosales[SUP] 7 8 [/SUP], Valentina Gonzalez-Jaramillo[SUP] 9 [/SUP], Abbas Bhuiya[SUP] 10 [/SUP], Daniel Reidpath[SUP] 11 [/SUP], Shamini Prathapan[SUP] 12 [/SUP], Sara Shahzad[SUP] 6 [/SUP], Christian L Althaus[SUP] 9 [/SUP], Nathalia Gonzalez-Jaramillo[SUP] 9 [/SUP], Oscar H Franco[SUP] 13 [/SUP], Global Dynamic Interventions Strategies for COVID-19 Collaborative Group
Affiliations
- PMID: 32430840
- DOI: 10.1007/s10654-020-00649-w
Abstract
To date, non-pharmacological interventions (NPI) have been the mainstay for controlling the coronavirus disease-2019 (COVID-19) pandemic. While NPIs are effective in preventing health systems overload, these long-term measures are likely to have significant adverse economic consequences. Therefore, many countries are currently considering to lift the NPIs-increasing the likelihood of disease resurgence. In this regard, dynamic NPIs, with intervals of relaxed social distancing, may provide a more suitable alternative. However, the ideal frequency and duration of intermittent NPIs, and the ideal "break" when interventions can be temporarily relaxed, remain uncertain, especially in resource-poor settings. We employed a multivariate prediction model, based on up-to-date transmission and clinical parameters, to simulate outbreak trajectories in 16 countries, from diverse regions and economic categories. In each country, we then modelled the impacts on intensive care unit (ICU) admissions and deaths over an 18-month period for following scenarios: (1) no intervention, (2) consecutive cycles of mitigation measures followed by a relaxation period, and (3) consecutive cycles of suppression measures followed by a relaxation period. We defined these dynamic interventions based on reduction of the mean reproduction number during each cycle, assuming a basic reproduction number (R[SUB]0[/SUB]) of 2.2 for no intervention, and subsequent effective reproduction numbers (R) of 0.8 and 0.5 for illustrative dynamic mitigation and suppression interventions, respectively. We found that dynamic cycles of 50-day mitigation followed by a 30-day relaxation reduced transmission, however, were unsuccessful in lowering ICU hospitalizations below manageable limits. By contrast, dynamic cycles of 50-day suppression followed by a 30-day relaxation kept the ICU demands below the national capacities. Additionally, we estimated that a significant number of new infections and deaths, especially in resource-poor countries, would be averted if these dynamic suppression measures were kept in place over an 18-month period. This multi-country analysis demonstrates that intermittent reductions of R below 1 through a potential combination of suppression interventions and relaxation can be an effective strategy for COVID-19 pandemic control. Such a "schedule" of social distancing might be particularly relevant to low-income countries, where a single, prolonged suppression intervention is unsustainable. Efficient implementation of dynamic suppression interventions, therefore, confers a pragmatic option to: (1) prevent critical care overload and deaths, (2) gain time to develop preventive and clinical measures, and (3) reduce economic hardship globally.
Keywords: COVID-19; Dynamic interventions; Epidemiology; Infectious disease; Prediction modelling.