Excess mortality associated with the COVID-19 pandemic during the 2020 and 2021 waves in Antananarivo, Madagascar.

INTRODUCTION: COVID-19-associated mortality remains difficult to estimate in sub-Saharan Africa because of the lack of comprehensive systems of death registration. Based on death registers referring to the capital city of Madagascar, we sought to estimate the excess mortality during the COVID-19 pandemic and calculate the loss of life expectancy. METHODS: Death records between 2016 and 2021 were used to estimate weekly excess mortality during the pandemic period. To infer its synchrony with circulation of SARS-CoV-2, a cross-wavelet analysis was performed. Life expectancy loss due to the COVID-19 pandemic was calculated by projecting mortality rates using the Lee and Carter model and extrapolating the prepandemic trends (1990-2019). Differences in life expectancy at birth were disaggregated by cause of death. RESULTS: Peaks of excess mortality in 2020-21 were associated with waves of COVID-19. Estimates of all-cause excess mortality were 38.5 and 64.9 per 100 000 inhabitants in 2020 and 2021, respectively, with excess mortality reaching ≥50% over 6 weeks. In 2021, we quantified a drop of 0.8 and 1.0 years in the life expectancy for men and women, respectively attributable to increased risks of death beyond the age of 60 years. CONCLUSION: We observed high excess mortality during the pandemic period, in particular around the peaks of SARS-CoV-2 circulation in Antananarivo. Our study highlights the need to implement death registration systems in low-income countries to document true toll of a pandemic.

Cross-sectional cycle threshold values reflect epidemic dynamics of COVID-19 in Madagascar.

As the national reference laboratory for febrile illness in Madagascar, we processed samples from the first epidemic wave of COVID-19, between March and September 2020. We fit generalized additive models to cycle threshold (Ct) value data from our RT-qPCR platform, demonstrating a peak in high viral load, low-Ct value infections temporally coincident with peak epidemic growth rates estimated in real time from publicly-reported incidence data and retrospectively from our own laboratory testing data across three administrative regions. We additionally demonstrate a statistically significant effect of duration of time since infection onset on Ct value, suggesting that Ct value can be used as a biomarker of the stage at which an individual is sampled in the course of an infection trajectory. As an extension, the population-level Ct distribution at a given timepoint can be used to estimate population-level epidemiological dynamics. We illustrate this concept by adopting a recently-developed, nested modeling approach, embedding a within-host viral kinetics model within a population-level Susceptible-Exposed-Infectious-Recovered (SEIR) framework, to mechanistically estimate epidemic growth rates from cross-sectional Ct distributions across three regions in Madagascar. We find that Ct-derived epidemic growth estimates slightly precede those derived from incidence data across the first epidemic wave, suggesting delays in surveillance and case reporting. Our findings indicate that public reporting of Ct values could offer an important resource for epidemiological inference in low surveillance settings, enabling forecasts of impending incidence peaks in regions with limited case reporting.

Epidemiological Patterns of Seasonal Respiratory Viruses during the COVID-19 Pandemic in Madagascar, March 2020-May 2022.

Three epidemic waves of coronavirus disease-19 (COVID-19) occurred in Madagascar from March 2020 to May 2022, with a positivity rate of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) of 21% to 33%. Our study aimed to identify the impact of COVID-19 on the epidemiology of seasonal respiratory viruses (RVs) in Madagascar. We used two different specimen sources (SpS). First, 2987 nasopharyngeal (NP) specimens were randomly selected from symptomatic patients between March 2020 and May 2022 who tested negative for SARS-CoV-2 and were tested for 14 RVs by multiplex real-time PCR. Second, 6297 NP specimens were collected between March 2020 and May 2022 from patients visiting our sentinel sites of the influenza sentinel network. The samples were tested for influenza, respiratory syncytial virus (RSV), and SARS-CoV-2. From SpS-1, 19% (569/2987) of samples tested positive for at least one RV. Rhinovirus (6.3%, 187/2987) was the most frequently detected virus during the first two waves, whereas influenza predominated during the third. From SpS-2, influenza, SARS-CoV-2, and RSV accounted for 5.4%, 24.5%, and 39.4% of the detected viruses, respectively. During the study period, we observed three different RV circulation profiles. Certain viruses circulated sporadically, with increased activity in between waves of SARS-CoV-2. Other viruses continued to circulate regardless of the COVID-19 situation. Certain viruses were severely disrupted by the spread of SARS-CoV-2. Our findings underline the importance and necessity of maintaining an integrated disease surveillance system for the surveillance and monitoring of RVs of public health interest.

Cross-sectional cycle threshold values reflect epidemic dynamics of COVID-19 in Madagascar.

As the national reference laboratory for febrile illness in Madagascar, we processed samples from the first epidemic wave of COVID-19, between March and September 2020. We fit generalized additive models to cycle threshold (C t ) value data from our RT-qPCR platform, demonstrating a peak in high viral load, low-C t value infections temporally coincident with peak epidemic growth rates estimated in real time from publicly-reported incidence data and retrospectively from our own laboratory testing data across three administrative regions. We additionally demonstrate a statistically significant effect of duration of time since infection onset on C t value, suggesting that C t value can be used as a biomarker of the stage at which an individual is sampled in the course of an infection trajectory. As an extension, the population-level C t distribution at a given timepoint can be used to estimate population-level epidemiological dynamics. We illustrate this concept by adopting a recently-developed, nested modeling approach, embedding a within-host viral kinetics model within a population-level Susceptible-Exposed-Infectious-Recovered (SEIR) framework, to mechanistically estimate epidemic growth rates from cross-sectional C t distributions across three regions in Madagascar. We find that C t -derived epidemic growth estimates slightly precede those derived from incidence data across the first epidemic wave, suggesting delays in surveillance and case reporting. Our findings indicate that public reporting of C t values could offer an important resource for epidemiological inference in low surveillance settings, enabling forecasts of impending incidence peaks in regions with limited case reporting.

The COVID-19 epidemic in Madagascar: clinical description and laboratory results of the first wave, march-september 2020.

BACKGROUND: Following the first detection of SARS-CoV-2 in passengers arriving from Europe on 19 March 2020, Madagascar took several mitigation measures to limit the spread of the virus in the country. METHODS: Nasopharyngeal and/or oropharyngeal swabs were collected from travellers to Madagascar, suspected SARS-CoV-2 cases and contact of confirmed cases. Swabs were tested at the national reference laboratory using real-time RT-PCR. Data collected from patients were entered in an electronic database for subsequent statistical analysis. All distribution of laboratory-confirmed cases were mapped, and six genomes of viruses were fully sequenced. RESULTS: Overall, 26,415 individuals were tested for SARS-CoV-2 between 18 March and 18 September 2020, of whom 21.0% (5,553/26,145) returned positive. Among laboratory-confirmed SARS-CoV-2-positive patients, the median age was 39 years (IQR: 28-52), and 56.6% (3,311/5,553) were asymptomatic at the time of sampling. The probability of testing positive increased with age with the highest adjusted odds ratio of 2.2 [95% CI: 1.9-2.5] for individuals aged 49 years and more. Viral strains sequenced belong to clades 19A, 20A and 20B indicative of several independent introduction of viruses. CONCLUSIONS: Our study describes the first wave of the COVID-19 in Madagascar. Despite early strategies in place Madagascar could not avoid the introduction and spread of the virus. More studies are needed to estimate the true burden of disease and make public health recommendations for a better preparation to another wave.

Monitoring for outbreak-associated excess mortality in an African city: Detection limits in Antananarivo, Madagascar.

OBJECTIVES: Quantitative estimates of the impact of infectious disease outbreaks are required to develop measured policy responses. In many low- and middle-income countries, inadequate surveillance and incompleteness of death registration are important barriers. DESIGN: Here, we characterize how large an impact on mortality would have to be for being detectable using the uniquely detailed mortality notification data from the city of Antananarivo, Madagascar, with application to a recent measles outbreak. RESULTS: The weekly mortality rate of children during the 2018-2019 measles outbreak was 161% above the expected value at its peak, and the signal can be detected earlier in children than in the general population. This approach to detect anomalies from expected baseline mortality allows us to delineate the prevalence of COVID-19 at which excess mortality would be detectable with the existing death notification system in Antananarivo. CONCLUSIONS: Given current age-specific estimates of the COVID-19 fatality ratio and the age structure of the population in Antananarivo, we estimate that as few as 11 deaths per week in the 60-70 years age group (corresponding to an infection rate of approximately 1%) would detectably exceed the baseline. Data from 2020 will undergo necessary processing and quality control in the coming months. Our results provide a baseline for interpreting this information.