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medrxiv; 2022.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2022.08.23.22279122


Background On March 11, 2020, WHO announced that the COVID-19 epidemic had passed the pandemic stage, indicating its spread over several continents. Tunisia’s containment and targeted screening strategy corresponded to the first WHO guidelines. Since then, public health policy has been more flexible and focused on the management of hospital beds. Objective Our aims are to analyze bed occupancies for public hospitals and time delay reponse from the health care system in regard to the epidemiological situation. Methods We have analyzed the evolution of daily cases in relation to the different NPI actions undertaken by the Tunisian Government between March 2019 and February 2022 using the CoMo model. We have also studied the flexibility of the O2 and ICU public hospital bed occupancies. We have used three distinct indices to assess this flexibility: the Ramp Duration Until the Peak (RDUP), which measures the duration of the wave/bed allocation effort. The Ramp Growth Until the Peak (RGUP), measures the peak height, and Ramp Rate Until the Peak (RRUP) measures the growth rate of the wave. Also, in order to evaluate the government response efficacy, we have calculated the time delay at the start (resp. at peak) of each two waves. Results The evolution of the epidemic in Tunisia was divided into two phases, the first of which corresponded to the initial wave, during which the pandemic was controlled due to very strong NPI actions. The second phase was distinguished by a progressive relaxation of measures and an increase in wave intensity. ICU bed availability has followed the demand for beds, while ICU bed occupancy has always been higher than 85% with a maximum of 97%. In terms of bed distribution, the government’s response was slow (9.4 days for the 02 beds and 18.2 days for the ICU beds). The same may be said for the reaction in terms of bed reallocation in the original departments (16 days for ICU beds and 10.6 days for O2 beds).. Conclusion We were able to examine the responsiveness of the system as a whole for all of Tunisia’s public hospitals by measuring the flexibility and bed margin. With this research, decision makers will be able to assess their response capabilities in the event of current pandemic, as well as a future one.

authorea preprints; 2021.


Background: . This paper presents, for the first time, the Epidemic Volatility Index (EVI), a conceptually simple, early warning tool for emerging epidemic waves. Methods: . EVI is based on the volatility of the newly reported cases per unit of time, ideally per day, and issues an early warning when the rate of the volatility change exceeds a threshold. Results: . Results from the COVID-19 epidemic in Italy and New York are presented here, while daily updated predictions for all world countries and each of the United States are available online. Interpretation . EVI’s application to data from the current COVID-19 pandemic revealed a consistent and stable performance in terms of detecting oncoming waves. The application of EVI to other epidemics and syndromic surveillance tasks in combination with existing early warning systems will enhance our ability to act fast and optimize containment of outbreaks.

Syndrome , COVID-19 , Encephalitis, Arbovirus