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

Rasambainarivo, Fidisoa; Rasoanomenjanahary, Anjarasoa; Rabarison, Joelinotahiana Hasina; Ramiadantsoa, Tanjona; Ratovoson, Rila; Randremanana, Rindra Vatosoa; Randrianarisoa, Santatra; Rajeev, Malavika; Masquelier, Bruno; Heraud, Jean‐Michel; Metcalf, C. Jessica E.; Rice, Benjamin L.

doi:10.1016/j.ijid.2020.11.182

Cited by 13 publications

(13 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A similar method was used to monitor excess mortality in Africa during a measles outbreak (Rasambainarivo et al, 2020). Despite significant fluctuations in NOCR data over the years, they seem to have remained stable since 1394 SH (Figure 1).…”

Section: Provincesmentioning

confidence: 99%

Excess deaths associated with the Iranian COVID-19 epidemic: A province-level analysis

Ghafari

Kadivar²

2021

International Journal of Infectious Diseases

View full text Add to dashboard Cite

There has been no province-level data on the number of coronavirus disease 2019 (COVID-19)related deaths in Iran since the start of the pandemic. This study was performed to estimate the number of COVID-19 deaths and population-level exposure per province using seasonal all-cause mortality data. Methods: Time-series data were collected from the National Organization for Civil Registration on the seasonal all-cause mortality from spring 2015 to summer 2020 (from March 21, 2015 to September 21, 2020), in accordance with the Solar Hijri (SH) calendar, to estimate the expected number of seasonal deaths for each province using a piecewise linear regression model. A population-weighted infection fatality ratio was then applied to estimate the level of exposure per province during this period. Results: From the start of winter to the end of summer (from December 22, 2019 to September 21, 2020), there were a total of 58 900 (95% confidence interval 46 900-69 500) excess deaths across all 31 provinces, with 27% (95% confidence interval 20-34%) estimated nationwide exposure to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In particular, Qom and Golestan were among the hardest-hit provinces, with nearly 57% exposure, while another 27 provinces showed significant levels of excess mortality in at least one season with >20% population-level exposure to the virus. Unexpectedly high levels of excess mortality were also detected during fall 2019 (from September 23 to December 21, 2019) across 18 provinces, unrelated and prior to the start of the COVID-19 pandemic. Conclusions: This study quantified the pattern of spread of COVID-19 across the country and identified areas with the largest epidemic growth requiring the most immediate interventions.

show abstract

Section: Provincesmentioning

confidence: 99%

Excess deaths associated with the Iranian COVID-19 epidemic: A province-level analysis

Ghafari

Kadivar²

2021

International Journal of Infectious Diseases

View full text Add to dashboard Cite

show abstract

“…Thus, excess mortality is the only proxy to assess the geographic spread of the epidemic across the country. Similar works have also been carried out to monitor excess mortality in Africa during a measles outbreak [6]. Despite significant fluctuations in NOCR's data over the years, they seem to have remained stable from 1394 SH onward (see Figure 1).…”

mentioning

confidence: 74%

Excess deaths associated with the Iranian COVID-19 epidemic: a province-level analysis

Ghafari

Kadivar²

2020

Preprint

View full text Add to dashboard Cite

BackgroundThe number of publicly reported deaths from COVID-19 may underestimate the true death toll from the epidemic as they rely on provisional data that are often incomplete or omit undocumented deaths from COVID-19. In addition, these reports may be subject to significant under-reporting due to a limited testing capacity of a country to identify suspect cases. This study estimated the number of seasonal excess deaths attributable to the COVID-19 epidemic in 31 provinces of Iran.MethodsWe gathered the nationwide and provincial time series of the seasonal all-cause mortality data from spring 2015 to summer 2020 (21 March 2015 to 21 September 2020), in accordance with the Solar Hijri (SH) calendar, from the National Organization for Civil Registration (NOCR). We estimated the expected number of seasonal deaths for each province using a piecewise linear regression model which we established based on the mortality figures for the previous years and considered any significant deviations from the expectation during winter, spring, and summer of 2020 to be directly associated with COVID-19.ResultsOur analysis shows that from the start of winter to the end of summer (from 22 December 2019 to 21 September 2020), there were a total of 58.9K (95%CI: 46.9K - 69.5K) excess deaths across all 31 provinces with 27% (95%CI: 20% - 34%) estimated nationwide exposure to SARS-CoV-2. In particular, 2 provinces in the central and northern Iran, namely Qom and Golestan, had the highest level of exposure with 57% (95%CI: 44% - 69%) and 56% (95%CI: 44% - 69%), respectively, while another 27 provinces had significant levels of excess mortality in at least one season with >20% population-level exposure to the virus. We also detected unexpectedly high levels of excess mortality during fall 2019 (from 23 September to 21 December 2019) across 18 provinces. Our findings suggest that this spike cannot be a result of an early cryptic transmission of COVID-19 across the country and is also inconsistent with the molecular phylogenetics estimates for the start of the pandemic and its arrival to Iran. However, in the absence of appropriate surveillance data for detecting severe acute respiratory infections we were unable to make a determination as to what caused the spike in fall 2019.Conflict of InterestNone.

show abstract

“…A major challenge in approaches of this kind is data availability. First, from the side of infectious disease data, there is limited availability of case numbers, and although many major cities in Madagascar have uniquely detailed mortality records (Masquelier et al, 2019) with scale and scope adequate to detect major outbreaks (Rasambainarivo et al, 2021) data compilation and accessibility to the research and public health community have lagged. To fill this gap in the landscape of public health communication in Madagascar, we developed a dashboard by collating data from daily televised reports, and this is the data that we use in our analyses.…”

Section: Discussionmentioning

confidence: 99%

Existing human mobility data sources poorly predicted the spatial spread of SARS-CoV-2 in Madagascar

Ramiadantsoa

Metcalf

Raherinandrasana

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

For emerging epidemics such as the COVID-19 pandemic, quantifying travel is a key component of developing accurate predictive models of disease spread to inform public health planning. However, in many LMICs, traditional data sets on travel such as commuting surveys as well as non-traditional sources such as mobile phone data are lacking, or, where available, have only rarely been leveraged by the public health community. Evaluating the accuracy of available data to measure transmission-relevant travel may be further hampered by limited reporting of suspected and laboratory confirmed infections. Here, we leverage case data collected as part of a COVID-19 dashboard collated via daily reports from the Malagasy authorities on reported cases of SARS-CoV-2 across the 22 regions of Madagascar. We compare the order of the timing of when cases were reported with predictions from a SARS-CoV-2 metapopulation model of Madagascar informed using various measures of connectivity including a gravity model based on different measures of distance, Internal Migration Flow data, and mobile phone data. Overall, the models based on mobile phone connectivity and the gravity-based on Euclidean distance best predicted the observed spread. The ranks of the regions most remote from the capital were more difficult to predict but interestingly, regions where the mobile phone connectivity model was more accurate differed from those where the gravity model was most accurate. This suggests that there may be additional features of mobility or connectivity that were consistently underestimated using all approaches, but are epidemiologically relevant. This work highlights the importance of data availability and strengthening collaboration among different institutions with access to critical data - models are only as good as the data that they use, so building towards effective data-sharing pipelines is essential.

show abstract

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

Cited by 13 publications

References 18 publications

Excess deaths associated with the Iranian COVID-19 epidemic: A province-level analysis

Excess deaths associated with the Iranian COVID-19 epidemic: A province-level analysis

Excess deaths associated with the Iranian COVID-19 epidemic: a province-level analysis

Existing human mobility data sources poorly predicted the spatial spread of SARS-CoV-2 in Madagascar

Contact Info

Product

Resources

About