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The projected impact of mitigation and suppression strategies on the COVID-19 epidemic in Senegal: A modelling study (preprint)
medrxiv; 2020.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2020.07.03.20144949
ABSTRACT

Background:

Physical distancing measures that reduce social contacts have formed a key part of national COVID-19 containment and mitigation strategies. Many Sub-Saharan African nations are now facing increasing numbers of cases of COVID-19 and there is a need to understand what levels of measures may be required to successfully reduce transmission.

Methods:

We collated epidemiological data along with information on key COVID-19 specific response policies and health system capacity estimates for services needed to treat COVID-19 patients in Senegal. We calibrated an age-structured SEIR model to these data to capture transmission dynamics accounting for demography, contact patterns, hospital capacity and disease severity. We simulated the impact of mitigation and suppression strategies focussed on reducing social contact rates.

Results:

Senegal acted promptly to contain the spread of SARS-CoV-2 and as a result has reduced the reproduction number from 1.9 (95% CI 1.7-2.2) to 1.3 (95% CI 1.2-1.5), which has slowed but not fully interrupted transmission. We estimate that continued spread is likely to peak in October, and to overwhelm the healthcare system with an estimated 77,400 deaths(95% CI 55,270-100,700). Further reductions in contact rates to suppress transmission (Rt<1) could significantly reduce this burden on healthcare services and improve overall health outcomes.

Conclusions:

Our results demonstrate that Senegal has already significantly reduced transmission. Enhanced physical distancing measures and rapid scale up of hospital capacity is likely to be needed to reduce mortality and protect healthcare infrastructure from high levels of demand.
Subject(s)

Full text: Available Collection: Preprints Database: medRxiv Main subject: COVID-19 Language: English Year: 2020 Document Type: Preprint

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Full text: Available Collection: Preprints Database: medRxiv Main subject: COVID-19 Language: English Year: 2020 Document Type: Preprint