ProCite
RefWorks
Reference Manager
BibTeX
Zotero
EndNote
-
1.
WHO , 2014. Early Detection, Assessment and Response to Acute Public Health Events: Implementation of Early Warning and Response with a Focus on Event-Based Surveillance. Geneva, Switzerland: World Health Organization.
-
2.
WHO , 2010. Early Warning Health Surveillance and Response. Geneva, Switzerland: World Health Organization.
-
3.
National Research Council (US) Committee on Climate Ecosystems, Infectious Diseases, and Human Health , 2001. Toward the development of disease early warning systems. Under the Weather: Climate, Ecosystems, and Infectious Disease. Washington, DC: National Academies Press.
-
4.
Rocklov J , Dubrow R , 2020. Climate change: an enduring challenge for vector-borne disease prevention and control. Nat Immunol 21: 479–483.
-
5.
Rocklov J , Tozan Y , 2019. Climate change and the rising infectiousness of dengue. Emerg Top Life Sci 3: 133–142.
-
6.
Bardosh KL , Ryan SJ , Ebi K , Welburn S , Singer B , 2017. Addressing vulnerability, building resilience: community-based adaptation to vector-borne diseases in the context of global change. Infect Dis Poverty 6: 166.
-
7.
Desai AN et al., 2019. Real-time epidemic forecasting: challenges and opportunities. Health Secur 17: 268–275.
-
8.
Lowe R , Bailey TC , Stephenson DB , Jupp TE , Graham RJ , Barcellos C , Carvalho MS , 2013. The development of an early warning system for climate-sensitive disease risk with a focus on dengue epidemics in southeast Brazil. Stat Med 32: 864–883.
-
9.
Thomson M , Indeje M , Connor S , Dilley M , Ward N , 2003. Malaria early warning in Kenya and seasonal climate forecasts. Lancet 362: 580.
-
10.
Thomson MC , Connor SJ , 2001. The development of malaria early warning systems for Africa. Trends Parasitol 17: 438–445.
-
11.
Thomson MC , Doblas-Reyes FJ , Mason SJ , Hagedorn R , Connor SJ , Phindela T , Morse AP , Palmer TN , 2006. Malaria early warnings based on seasonal climate forecasts from multi-model ensembles. Nature 439: 576–579.
-
12.
Thomson MC , Mason SJ , Phindela T , Connor SJ , 2005. Use of rainfall and sea surface temperature monitoring for malaria early warning in Botswana. Am J Trop Med Hyg 73: 214–221.
-
13.
Hii YL , Rocklov J , Wall S , Ng LC , Tang CS , Ng N , 2012. Optimal lead time for dengue forecast. PLoS Negl Trop Dis 6: e1848.
-
14.
Hii YL , Zhu H , Ng N , Ng LC , Rocklov J , 2012. Forecast of dengue incidence using temperature and rainfall. PLoS Negl Trop Dis 6: e1908.
-
15.
Ramadona AL , Lazuardi L , Hii YL , Holmner A , Kusnanto H , Rocklov J , 2016. Prediction of dengue outbreaks based on disease surveillance and meteorological data. PLoS One 11: e0152688.
-
16.
Runge-Ranzinger S , Kroeger A , Olliaro P , McCall PJ , Sanchez Tejeda G , Lloyd LS , Hakim L , Bowman LR , Horstick O , Coelho G , 2016. Dengue contingency planning: from research to policy and practice. PLoS Negl Trop Dis 10: e0004916.
-
17.
Ninphanomchai S , Chansang C , Hii YL , Rocklov J , Kittayapong P , 2014. Predictiveness of disease risk in a global outreach tourist setting in Thailand using meteorological data and vector-borne disease incidences. Int J Environ Res Public Health 11: 10694–10709.
-
18.
Ebi KL , Ebi KL , Burton I , Glenn R & McGregor GR Biometeorology for Adaptation to Climate Variability and Change. New York, NY: Springer, 49–74.
-
19.
WHO , 2001. Malaria Early Warning Systems—A Framework for Field Research in Africa. Geneva, Switzerland: World Health Organization.
-
20.
Bowman LR et al., 2016. Alarm variables for Dengue outbreaks: a multi-centre study in Asia and Latin America. PLoS One 11: e0157971.
-
21.
Cullen J , Chitprarop U , Doberstyn E , Sombatwattanangkul K , 1984. An epidemiological early warning system for malaria control in northern Thailand. Bull World Health Organ 62: 107.
-
22.
Davis RG , Kamanga A , Castillo-Salgado C , Chime N , Mharakurwa S , Shiff C , 2011. Early detection of malaria foci for targeted interventions in endemic southern Zambia. Malar J 10: 260.
-
23.
Teklehaimanot HD , Schwartz J , Teklehaimanot A , Lipsitch M , 2004. Weather-based prediction of Plasmodium falciparum malaria in epidemic-prone regions of Ethiopia II. Weather-based prediction systems perform comparably to early detection systems in identifying times for interventions. Malar J 3: 44.
-
24.
Grover-Kopec E , Kawano M , Klaver RW , Blumenthal B , Ceccato P , Connor SJ , 2005. An online operational rainfall-monitoring resource for epidemic malaria early warning systems in Africa. Malar J 4: 6.
-
25.
Githeko AK , Ndegwa W , 2001. Predicting malaria epidemics in the Kenyan highlands using climate data: a tool for decision makers. Glob Change Hum Health 2: 54–63.
-
26.
Cox J , Abeku TA , 2007. Early warning systems for malaria in Africa: from blueprint to practice. Trends Parasitol 23: 243–246.
-
27.
UNEP , 2012. Early Warning Systems: A State of the Art Analysis and Future Directions. Nairobi, Kenya: Division of Early Warning and Assessment, United Nations Environment Programme.
-
28.
Ebi KL , Hess JJ , Isaksen TB , 2016. Using uncertain climate and development information in health adaptation planning. Curr Environ Health Rep 3: 99–105.
-
29.
Brown Z , Kramer R , Mutero C , Kim D , Miranda ML , Ameneshewa B , Lesser A , Paul CJ , 2012. Stakeholder development of the malaria decision analysis support tool (MDAST). Malar J 11: 1.
-
30.
Kramer RA , Dickinson KL , Anderson RM , Fowler VG , Miranda ML , Mutero CM , Saterson KA , Wiener JB , 2009. Using decision analysis to improve malaria control policy making. Health Policy 92: 133–140.
-
31.
Cellini SR , Kee JE , Wholey JS , Hatry HP & Newcomer KE Handbook of Practical Program Evaluation, 3rd edition. Hoboken, NJ: Wiley, 493–530.
-
32.
Culyer AJ , Chalkidou K , 2019. Economic evaluation for health investments en route to universal health coverage: cost-benefit analysis or cost-effectiveness analysis? Value Health 22: 99–103.
-
33.
Fuguitt D , Fuguitt DJ , Wilcox SJ , 1999. Cost-Benefit Analysis for Public Sector Decision Makers. Westport, CT: Greenwood Publishing Group.
-
34.
Priemus H , Flyvbjerg B , van Wee B , 2008. Decision-Making on Mega-projects: Cost-Benefit Analysis, Planning and Innovation. Cheltenham, United Kingdom: Edward Elgar Publishing.
-
35.
Brown DK , 2004. Cost-benefit analysis in criminal law. Calif Law Rev 92: 323.
-
36.
Tai B-WB , Bae YH , Le QA , 2016. A systematic review of health economic evaluation studies using the patient’s perspective. Value Health 19: 903–908.
-
37.
Lowe R , Bailey TC , Stephenson DB , Graham RJ , Coelho CAS , Sá Carvalho M , Barcellos C , 2011. Spatio-temporal modelling of climate-sensitive disease risk: towards an early warning system for dengue in Brazil. Comput Geosci 37: 371–381.
-
38.
Bowman LR , Donegan S , McCall PJ , 2016. Is dengue vector control deficient in effectiveness or evidence?: Systematic review and meta-analysis. PLoS Negl Trop Dis 10: e0004551.
-
39.
Fox-Rushby J , Cairns J , 2005. Economic Evaluation. New York, NY: Maidenhead.
-
40.
Adalsteinsson E , Toumi M , 2013. Benefits of probabilistic sensitivity analysis – a review of NICE decisions. J Mark Access Health Policy 1. doi: 10.3402/jmahp.v1i0.21240.
-
41.
Rogers D , Tsirkunov V , 2011. Costs and benefits of early warning systems. Global Assessment Report on Disaster Risk Reduction. New York, NY: United Nations. 1–16.
-
42.
Anker M , 2005. Evaluating the Costs and Benefits of National Surveillance and Response Systems. Methodologies and Options. Geneva, Switzerland: World Health Organization.
-
43.
Somda ZC , Perry HN , Messonnier NR , Djingarey MH , Ki SO , Meltzer MI , 2010. Modeling the cost-effectiveness of the integrated disease surveillance and response (IDSR) system: meningitis in Burkina Faso. PLoS One 5: e13044.
-
44.
McConnell KJ , 2001. Evaluating Health Policy under Uncertainty: An Application to Early Warning Systems. Stanford, CA: Stanford University.
-
45.
Viboud C , Vespignani A , 2019. The future of influenza forecasts. Proc Natl Acad Sci USA 116: 2802–2804.
-
46.
Zaitchik BF et al., 2020. A framework for research linking weather, climate and COVID-19. Nat Commun 11: 5730.
| Past two years | Past Year | Past 30 Days | |
|---|---|---|---|
| Abstract Views | 962 | 962 | 608 |
| Full Text Views | 128 | 128 | 16 |
| PDF Downloads | 742 | 742 | 69 |
A Methodological Framework for Economic Evaluation of Operational Response to Vector-Borne Diseases Based on Early Warning Systems
ABSTRACT.
Despite significant advances in improving the predictive models for vector-borne diseases, only a few countries have integrated an early warning system (EWS) with predictive and response capabilities into their disease surveillance systems. The limited understanding of forecast performance and uncertainties by decision-makers is one of the primary factors that precludes its operationalization in preparedness and response planning. Further, predictive models exhibit a decrease in forecast skill with longer lead times, a trade-off between forecast accuracy and timeliness and effectiveness of action. This study presents a methodological framework to evaluate the economic value of EWS-triggered responses from the health system perspective. Assuming an operational EWS in place, the framework makes explicit the trade-offs between forecast accuracy, timeliness of action, effectiveness of response, and costs, and uses the net benefit analysis, which measures the benefits of taking action minus the associated costs. Uncertainty in disease forecasts and other parameters is accounted for through probabilistic sensitivity analysis. The output is the probability distribution of the net benefit estimates at given forecast lead times. A non-negative net benefit and the probability of yielding such are considered a general signal that the EWS-triggered response at a given lead time is economically viable. In summary, the proposed framework translates uncertainties associated with disease forecasts and other parameters into decision uncertainty by quantifying the economic risk associated with operational response to vector-borne disease events of potential importance predicted by an EWS. The goal is to facilitate a more informed and transparent public health decision-making under uncertainty.
Author Notes
Authors’ addresses: Yesim Tozan and Sooyoung Kim, School of Global Public Health, New York University, New York, NY, E-mails: tozan@nyu.edu and sk9076@nyu.edu. Maquines Sewe, Department of Public Health and Clinical Medicine, Epidemiology and Global Health & Umea○ Centre for Global Health Research, Umea○ University, Umea○, Sweden, E-mail: maquines.sewe@umu.se. Joacim Rocklöv, Department of Public Health and Clinical Medicine, Epidemiology and Global Health & Umea○ Centre for Global Health Research, Umea○ University, Umea○, Sweden, and Heidelberg Institute of Global Health, Interdisciplinary Centre for Scientific Computing, Heidelberg University, Heidelberg, Germany, E-mail: joacim.rockloev@uni-heidelberg.de.