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Thakkar N, Abubakar AHA, Shube M, Jama MA, Derow M, Lambach P, Ashmony H, Farid M, Sim SY, O’Connor P, Minta A, Bose AS, Musanhu P, Hasan Q, Bar-Zeev N, Malik SMMR. Estimating the Impact of Vaccination Campaigns on Measles Transmission in Somalia. Vaccines (Basel) 2024; 12:314. [PMID: 38543948 PMCID: PMC10974214 DOI: 10.3390/vaccines12030314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Revised: 03/12/2024] [Accepted: 03/13/2024] [Indexed: 04/21/2024] Open
Abstract
Somalia is a complex and fragile setting with a demonstrated potential for disruptive, high-burden measles outbreaks. In response, since 2018, Somalian authorities have partnered with UNICEF and the WHO to implement measles vaccination campaigns across the country. In this paper, we create a Somalia-specific model of measles transmission based on a comprehensive epidemiological dataset including case-based surveillance, vaccine registries, and serological surveys. We use this model to assess the impact of these campaign interventions on Somalian's measles susceptibility, showing, for example, that across the roughly 10 million doses delivered, 1 of every 5 immunized a susceptible child. Finally, we use the model to explore a counter-factual epidemiology without the 2019-2020 campaigns, and we estimate that those interventions prevented over 10,000 deaths.
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Affiliation(s)
- Niket Thakkar
- Institute for Disease Modeling, Bill & Melinda Gates Foundation, Seattle, WA 98109, USA
| | | | - Mukhtar Shube
- Federal Ministry of Health, Mogadishu P.O. Box 22, Somalia
| | | | - Mohamed Derow
- Federal Ministry of Health, Mogadishu P.O. Box 22, Somalia
| | | | | | | | - So Yoon Sim
- World Health Organization, 1202 Geneva, Switzerland
| | | | - Anna Minta
- World Health Organization, 1202 Geneva, Switzerland
| | | | | | - Quamrul Hasan
- World Health Organization, Regional Office for the Eastern Mediterranean, Cairo 11371, Egypt
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Ramiadantsoa T, Metcalf CJE, Raherinandrasana AH, Randrianarisoa S, Rice BL, Wesolowski A, Randriatsarafara FM, Rasambainarivo F. Existing human mobility data sources poorly predicted the spatial spread of SARS-CoV-2 in Madagascar. Epidemics 2021; 38:100534. [PMID: 34915300 PMCID: PMC8641444 DOI: 10.1016/j.epidem.2021.100534] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 09/29/2021] [Accepted: 12/02/2021] [Indexed: 12/24/2022] Open
Abstract
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.
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Affiliation(s)
- Tanjona Ramiadantsoa
- Department of Life Science, University of Fianarantsoa, Madagascar; Department of Mathematics, University of Fianarantsoa, Madagascar; Department of Integrative Biology, University of Wisconsin-Madison, WI, USA.
| | - C Jessica E Metcalf
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA; Princeton School of Public and International Affairs, Princeton University, NJ, USA
| | - Antso Hasina Raherinandrasana
- Surveillance Unit, Ministry of Health of Madagascar, Madagascar; Faculty of Medicine, University of Antananarivo, Madagascar
| | | | - Benjamin L Rice
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA; Madagascar Health and Environmental Research (MAHERY), Maroantsetra, Madagascar
| | - Amy Wesolowski
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | | | - Fidisoa Rasambainarivo
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA; Mahaliana Labs SARL, Antananarivo, Madagascar
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3
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Ramiadantsoa T, Metcalf CJE, Raherinandrasana AH, Randrianarisoa S, Rice BL, Wesolowski A, Randriatsarafara FM, Rasambainarivo F. Existing human mobility data sources poorly predicted the spatial spread of SARS-CoV-2 in Madagascar. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2021:2021.07.30.21261392. [PMID: 34373863 PMCID: PMC8351785 DOI: 10.1101/2021.07.30.21261392] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
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.
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Affiliation(s)
- Tanjona Ramiadantsoa
- Department of Life Science, University of Fianarantsoa, Madagascar
- Department of Mathematics, University of Fianarantsoa, Madagascar
- Department of Integrative Biology, University of Wisconsin-Madison, WI, USA
| | - C Jessica E Metcalf
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
- Princeton School of Public and International Affairs, Princeton University, NJ, USA
| | | | | | - Benjamin L Rice
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
- Madagascar Health and Environmental Research (MAHERY), Maroantsetra, Madagascar
| | - Amy Wesolowski
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | | | - Fidisoa Rasambainarivo
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
- Mahaliana Labs SARL, Antananarivo, Madagascar
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Abstract
AbstractReproduction, mortality, and immune function often change with age but do not invariably deteriorate. Across the tree of life, there is extensive variation in age-specific performance and changes to key life-history traits. These changes occur on a spectrum from classic senescence, where performance declines with age, to juvenescence, where performance improves with age. Reproduction, mortality, and immune function are also important factors influencing the spread of infectious disease, yet there exists no comprehensive investigation into how the aging spectrum of these traits impacts epidemics. We used a model laboratory infection system to compile an aging profile of a single organism, including traits directly linked to pathogen susceptibility and those that should indirectly alter pathogen transmission by influencing demography. We then developed generalizable epidemiological models demonstrating that different patterns of aging produce dramatically different transmission landscapes: in many cases, aging can reduce the probability of epidemics, but it can also promote severity. This work provides context and tools for use across taxa by empiricists, demographers, and epidemiologists, advancing our ability to accurately predict factors contributing to epidemics or the potential repercussions of senescence manipulation.
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Goodson JL. Recent setbacks in measles elimination: the importance of investing in innovations for immunizations. Pan Afr Med J 2020; 35:15. [PMID: 32373266 PMCID: PMC7196335 DOI: 10.11604/pamj.supp.2020.35.1.21740] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2020] [Accepted: 02/13/2020] [Indexed: 11/18/2022] Open
Abstract
The recent setbacks in efforts to achieve measles elimination goals are alarming. To reverse the current trends, it is imperative that the global health community urgently intensify efforts and make resource commitments to implement evidence-based elimination strategies fully, including supporting research and innovations. The Immunization Agenda 2030: A Global Strategy to Leave No One Behind (IA2030) is the new global guidance document that builds on lessons learned and progress made toward the GVAP goals, includes research and innovation as a core strategic priority, and identifies measles as a “tracer” for improving immunisation services and strengthening primary health care systems. To achieve vaccination coverage and equity targets that leave no one behind, and accelerate progress toward disease eradication and elimination goals, sustained and predictable investments are needed for the identified research and innovations priorities for the new decade.
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Affiliation(s)
- James L Goodson
- Accelerated Disease Control and Surveillance Branch, Global Immunization Division, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
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