Fidisoa Rasambainarivo1, Anjarasoa Rasoanomenjanahary2, Joelinotahiana Hasina Rabarison3, Tanjona Ramiadantsoa4, Rila Ratovoson3, Rindra Randremanana3, Santatriniaina Randrianarisoa5, Malavika Rajeev6, Bruno Masquelier7, Jean Michel Heraud3, C Jessica E Metcalf8, Benjamin L Rice9. 1. Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA; Mahaliana Labs SARL, Antananarivo, Madagascar. 2. Bureau Municipal d'Hygiène de la Commune Urbaine d'Antananarivo, Madagascar. 3. Institut Pasteur de Madagascar, Antananarivo, Madagascar. 4. Department of Mathematics, University of Fianarantsoa, Madagascar; Department of Life Sciences, University of Fianarantsoa, Madagascar. 5. Mahaliana Labs SARL, Antananarivo, Madagascar. 6. Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA. 7. Université Catholique de Louvain, Louvain-La-Neuve, Belgium; Institut National d'Études Démographiques, France. 8. Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA; Princeton School of Public and International Affairs, Princeton University, NJ, USA. 9. Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA; Madagascar Health and Environmental Research (MAHERY), Maroantsetra, Madagascar.
Abstract
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.
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.
Authors: Kayla Kauffman; Courtney S Werner; Georgia Titcomb; Michelle Pender; Jean Yves Rabezara; James P Herrera; Julie Teresa Shapiro; Alma Solis; Voahangy Soarimalala; Pablo Tortosa; Randall Kramer; James Moody; Peter J Mucha; Charles Nunn Journal: J R Soc Interface Date: 2022-01-12 Impact factor: 4.118
Authors: Fidisoa Rasambainarivo; Tanjona Ramiadantsoa; Antso Raherinandrasana; Santatra Randrianarisoa; Benjamin L Rice; Michelle V Evans; Benjamin Roche; Fidiniaina Mamy Randriatsarafara; Amy Wesolowski; Jessica C Metcalf Journal: BMC Public Health Date: 2022-04-12 Impact factor: 4.135