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Shankar M, Hartner AM, Arnold CRK, Gayawan E, Kang H, Kim JH, Gilani GN, Cori A, Fu H, Jit M, Muloiwa R, Portnoy A, Trotter C, Gaythorpe KAM. How mathematical modelling can inform outbreak response vaccination. BMC Infect Dis 2024; 24:1371. [PMID: 39617902 PMCID: PMC11608489 DOI: 10.1186/s12879-024-10243-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Academic Contribution Register] [Received: 09/25/2024] [Accepted: 11/18/2024] [Indexed: 12/13/2024] Open
Abstract
Mathematical models are established tools to assist in outbreak response. They help characterise complex patterns in disease spread, simulate control options to assist public health authorities in decision-making, and longer-term operational and financial planning. In the context of vaccine-preventable diseases (VPDs), vaccines are one of the most-cost effective outbreak response interventions, with the potential to avert significant morbidity and mortality through timely delivery. Models can contribute to the design of vaccine response by investigating the importance of timeliness, identifying high-risk areas, prioritising the use of limited vaccine supply, highlighting surveillance gaps and reporting, and determining the short- and long-term benefits. In this review, we examine how models have been used to inform vaccine response for 10 VPDs, and provide additional insights into the challenges of outbreak response modelling, such as data gaps, key vaccine-specific considerations, and communication between modellers and stakeholders. We illustrate that while models are key to policy-oriented outbreak vaccine response, they can only be as good as the surveillance data that inform them.
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Affiliation(s)
- Manjari Shankar
- Medical Research Council Centre for Global Infectious Disease Analysis, Imperial College London, London, UK.
| | - Anna-Maria Hartner
- Medical Research Council Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
- Centre for Artificial Intelligence in Public Health Research, Robert Koch Institute, Wildau, Germany
| | - Callum R K Arnold
- Center for Infectious Disease Dynamics, Pennsylvania State University, University Park, 16802, PA, USA
| | - Ezra Gayawan
- Department of Statistics, Federal University of Technology, Akure, Nigeria
| | - Hyolim Kang
- Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK
| | - Jong-Hoon Kim
- Department of Epidemiology, Public Health, Impact, International Vaccine Institute, Seoul, South Korea
| | - Gemma Nedjati Gilani
- Medical Research Council Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Anne Cori
- Medical Research Council Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Han Fu
- Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK
| | - Mark Jit
- Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK
- School of Public Health, University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Rudzani Muloiwa
- Department of Paediatrics & Child Health, Faculty of Health Sciences, University of Cape Town, Red Cross War Memorial Children's Hospital, Cape Town, South Africa
| | - Allison Portnoy
- Department of Global Health, Boston University School of Public Health, Boston, United States
- Center for Health Decision Science, Harvard T.H. Chan School of Public Health, Boston, United States
| | - Caroline Trotter
- Medical Research Council Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
- Department of Veterinary Medicine and Pathology, University of Cambridge, Cambridge, UK
| | - Katy A M Gaythorpe
- Medical Research Council Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
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Calderon JS, Perry KE, Thi SS, Stevens LL. Innovating tuberculosis prevention to achieve universal health coverage in the Philippines. THE LANCET REGIONAL HEALTH. WESTERN PACIFIC 2022; 29:100609. [PMID: 36605879 PMCID: PMC9808427 DOI: 10.1016/j.lanwpc.2022.100609] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Academic Contribution Register] [Indexed: 11/05/2022]
Abstract
To contribute to tuberculosis (TB) elimination, TB preventive treatment (TPT) should integrate innovative approaches including tele-contact investigation (TCI), mathematical modelling, and participatory governance. Aligning with the World Health Organisation's primary health care framework, supply is provided by the provincial health system, demand is cultivated by the community, while governance is represented by the governor, who oversees the health leadership structure, local policies, and allocation of resources. A healthy dynamic between these three components is required to achieve universal health coverage (UHC). Because of their potential to integrate health systems and engage communities, primary health care principles underpin an effective approach to TB prevention. First, the provincial health system should connect with the community through TCI to transform the status quo of passive service delivery. Second, community participation should strengthen the linkage between the health system and governance, which ensures that community action plans are aligned with provincial TPT targets. Third, governance should leverage mathematical modelling to allocate resources to those with greatest need. Central to this is a reliable TB information system that should validate a robust mathematical model to measure cost-effectiveness of the intervention. Collectively, this holistic approach to TB prevention could provide a proof-of-concept that investing in primary health care is the key to UHC.
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Affiliation(s)
| | | | - Sein Sein Thi
- FHI 360 Asia Pacific Regional Office, Bangkok, Thailand
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Panda S. Looking back to move forward: A travel rule underlined by the current pandemic. Indian J Public Health 2022; 66:403-406. [PMID: 37039163 DOI: 10.4103/ijph.ijph_1513_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Academic Contribution Register] [Indexed: 01/01/2023] Open
Abstract
Learning from the past - is easier said than done. In this narrative, "travel" refers to the forward movement of the society at large on the path of health and development. It is suggested that looking back and learning from the lived experiences of the past outbreaks could help generating public health insights and incorporating them in planning for a better future. In the process, a country may choose to revisit what took place in the recent past during the COVID-19 pandemic within its boundary and beyond. However, unfolding of events in the past, which is not as immediate as COVID neither too far as the flu pandemic of 1918, also has lessons to offer. Recognizably, a few alarms, that rang in the recent past and cried for mass attention towards beefed up public health preparedness, were missed. It is therefore necessary now to critically examine the past-efforts to eradicate, eliminate or control diseases such as small pox, polio, HIV, tuberculosis, leprosy, measles or malaria. Results of such evaluation could inform the future courses of actions around disease elimination science and health (DESH) and help develop better nations.
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