Temporal Dynamics in COVID-19 Transmission: Case of Some African Countries

Coronavirus disease 2019 (COVID-19) has become a global threat to public health and economy. The potential burden of this pandemic in developing world, particularly the African countries, is much concerning. With the aim of providing supporting evidence for decision making, this paper studies the dynamics of COVID-19 transmission through time in selected African countries. Time-dependent reproduction number (Rt) is one of the tools employed to quantify temporal dynamics of the disease. Pattern of the estimated reproduction numbers showed that transmissibility of the disease has been fluctuating through time in most of the countries included in this study. In few countries such as South Africa and Democratic Republic of Congo (DRC), these estimates dropped quickly and stayed stable, but greater than 1, for months. Regardless of their variability through time, the estimated reproduction numbers remain greater than or nearly equal to 1 in all countries. Another Statistical model used in this study, namely Autoregressive Conditional Poisson (ACP) model, showed that expected (mean) number of new cases is significantly dependent on short range change in new cases in all countries. In countries where there is no persistent trend in new cases, current mean number of new cases (on day t) depend on both previous observation and previous mean (day t - 1). In countries where there is continued trend in new cases, current mean is more affected by number of new cases on preceding day.

Hospital bed capacity across in Tunisia hospital during the first 4 waves of the COVID-19 pandemic:A descriptive analysis

Background:In March 2020,the WHO declared COVID-19 as a pandemic,and Tunisia implemented a containment and targeted screening strategy.The country’s public health policy has since focused on managing hospital beds.Methods:The study analyzed the bed occupancy rates in public hospitals in Tunisia during the pandemic.The evolution of daily cases and nonpharmaceutical interventions(NPI)actions undertaken by the Tunisian Government were also analyzed.The study used 3 indices to assess bed flexibility:Ramp duration until the peak,ramp growth until the peak,and ramp rate until the peak.The study also calculated the time shift at the start and peak of each wave to evaluate the government’s response efficacy.Results:The study found that the evolution of the epidemic in Tunisia had 2 phases.The first phase saw the pandemic being controlled due to strong NPI actions,while the second phase saw a relaxation of measures and an increase in wave intensity.ICU bed availability followed the demand for beds,but ICU bed occupancy remained high,with a maximum of 97%.The government’s response in terms of bed distribution and reallocation was slow.The study found that the most deadly wave by ICU occupied bed was the third wave due to a historical variant,while the fifth wave due to the delta variant was the most deadly in terms of cumulative death.Conclusions:The study concluded that decision-makers could use its findings to assess their response capabilities in the current pandemic and future ones.The study highlighted the importance of flexible and responsive healthcare systems in managing pandemics.

Dynamic variations in COVID-19 with the SARS-CoV-2 Omicron variant in Kazakhstan and Pakistan

Background The ongoing coronavirus disease 2019(COVID-19)pandemic caused by the severe acute respiratory syndrome-coronavirus 2(SARS-CoV-2)and the Omicron variant presents a formidable challenge for control and prevention worldwide,especially for low-and middle-income countries(LMICs).Hence,taking Kazakhstan and Pakistan as examples,this study aims to explore COVID-19 transmission with the Omicron variant at different contact,quarantine and test rates.Methods A disease dynamic model was applied,the population was segmented,and three time stages for Omicron transmission were established:the initial outbreak,a period of stabilization,and a second outbreak.The impact of population contact,quarantine and testing on the disease are analyzed in five scenarios to analysis their impacts on the disease.Four statistical metrics are employed to quantify the model’s performance,including the correlation coefficient(CC),normalized absolute error,normalized root mean square error and distance between indices of simulation and observation(DISO).Results Our model has high performance in simulating COVID-19 transmission in Kazakhstan and Pakistan with high CC values greater than 0.9 and DISO values less than 0.5.Compared with the present measures(baseline),decreasing(increasing)the contact rates or increasing(decreasing)the quarantined rates can reduce(increase)the peak values of daily new cases and forward(delay)the peak value times(decreasing 842 and forward 2 days for Kazakhstan).The impact of the test rates on the disease are weak.When the start time of stage Ⅱ is 6 days,the daily new cases are more than 8 and 5 times the rate for Kazakhstan and Pakistan,respectively(29,573 vs.3259;7398 vs.1108).The impact of the start times of stageⅢon the disease are contradictory to those of stageⅡ.Conclusions For the two LMICs,Kazakhstan and Pakistan,stronger control and prevention measures can be more effective in combating COVID-19.Therefore,to reduce Omicron transmission,strict management of population movement should be employed.Moreover,the timely application of these strategies also plays a key role in disease control.