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A nowcasting framework for correcting for reporting delays in malaria surveillance.

ABSTRACT: Time lags in reporting to national surveillance systems represent a major barrier for the control of infectious diseases, preventing timely decision making and resource allocation. This issue is particularly acute for infectious diseases like malaria, which often impact rural and remote communities the hardest. In Guyana, a country located in South America, poor connectivity among remote malaria-endemic regions hampers surveillance efforts, making reporting delays a key challenge for elimination. Here, we analyze 13 years of malaria surveillance data, identifying key correlates of time lags between clinical cases occurring and being added to the central data system. We develop nowcasting methods that use historical patterns of reporting delays to estimate occurred-but-not-reported monthly malaria cases. To assess their performance, we implemented them retrospectively, using only information that would have been available at the time of estimation, and found that they substantially enhanced the estimates of malaria cases. Specifically, we found that the best performing models achieved up to two-fold improvements in accuracy (or error reduction) over known cases in selected regions. Our approach provides a simple, generalizable tool to improve malaria surveillance in endemic countries and is currently being implemented to help guide existing resource allocation and elimination efforts.

SUBMITTER: Menkir TF

PROVIDER: S-EPMC8659367 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature

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A nowcasting framework for correcting for reporting delays in malaria surveillance.

Menkir Tigist F TF Cox Horace H Poirier Canelle C Saul Melanie M Jones-Weekes Sharon S Clementson Collette C M de Salazar Pablo P Santillana Mauricio M Buckee Caroline O CO

PLoS computational biology 20211116 11

Time lags in reporting to national surveillance systems represent a major barrier for the control of infectious diseases, preventing timely decision making and resource allocation. This issue is particularly acute for infectious diseases like malaria, which often impact rural and remote communities the hardest. In Guyana, a country located in South America, poor connectivity among remote malaria-endemic regions hampers surveillance efforts, making reporting delays a key challenge for elimination ...[more]

PMID: 34784353

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Project description:The objective of this study was to investigate the use of novel surveillance tools in a malaria endemic region where prevalence information is limited. Specifically, online reporting for participatory epidemiology was used to gather information about malaria spread directly from the public. Individuals in India were incentivized to self-report their recent experience with malaria by micro-monetary payments.Self-reports about malaria diagnosis status and related information were solicited online via Amazon's Mechanical Turk. Responders were paid $0.02 to answer survey questions regarding their recent experience with malaria. Timing of the peak volume of weekly self-reported malaria diagnosis in 2010 was compared to other available metrics such as the volume over time of and information about the epidemic from media sources. Distribution of Plasmodium species reports were compared with values from the literature. The study was conducted in summer 2010 during a malaria outbreak in Mumbai and expanded to other cities during summer 2011, and prevalence from self-reports in 2010 and 2011 was contrasted.Distribution of Plasmodium species diagnosis through self-report in 2010 revealed 59% for Plasmodium vivax, which is comparable to literature reports of the burden of P. vivax in India (between 50 and 69%). Self-reported Plasmodium falciparum diagnosis was 19% and during the 2010 outbreak and the estimated burden was between 10 and 15%. Prevalence between 2010 and 2011 via self-reports decreased significantly from 36.9% to 19.54% in Mumbai (p = 0.001), and official reports also confirmed a prevalence decrease in 2011.With careful study design, micro-monetary incentives and online reporting are a rapid way to solicit malaria, and potentially other public health information. This methodology provides a cost-effective way of executing a field study that can act as a complement to traditional public health surveillance methods, offering an opportunity to obtain information about malaria activity, temporal progression, demographics affected or Plasmodium-specific diagnosis at a finer resolution than official reports can provide. The recent adoption of technologies, such as the Internet supports self-reporting mediums, and self-reporting should continue to be studied as it can foster preventative health behaviours.

Project description:BackgroundTo achieve malaria elimination in the Greater Mekong Subregion, including Myanmar, it is necessary to ensure all malaria cases are detected, treated, and reported in a timely manner. Mobile phone-based applications for malaria reporting, case management, and surveillance implemented at a community-level may overcome reporting limitations associated with current paper-based reporting (PBR), but their effectiveness in this context is unknown.MethodsA mixed methods evaluation study was undertaken to determine the effectiveness of a national Malaria Case-Based Reporting (MCBR) mobile phone application in improving malaria case reporting compared to the existing PBR reporting system in Myanmar. Methods included secondary analysis of malaria case report data, questionnaires, focus group discussions and field observations of community volunteers, interviews and direct observations of malaria programme stakeholders, and cost analysis. Using a combination of these approaches the following areas were investigated: data quality and completeness, data access and usage, capacity for timely reporting, the acceptability, functionality, and ease of use of the application and facilitators and barriers to its use, and the relative cost of MCBR compared to the PBR system.ResultsCompared to PBR, MCBR enabled more accurate and complete data to be reported in a much timelier manner, with 63% of MCBR users reporting they transmit rapid diagnostic test outcomes within 24 h, compared to 0% of PBR users. MCBR was favoured by integrated community malaria volunteers and their supervisors because of its efficiency. However, several technical and operational challenges associated with internet coverage, data transmission, and e-literacy were identified and stakeholders reported not being confident to rely solely on MCBR data for programmatic decision-making.ConclusionsImplementation of MCBR provided timely and accurate data for malaria surveillance. Findings from this evaluation study will enable the optimization of an application-based reporting system for malaria monitoring and surveillance in the Greater Mekong Subregion and advance systems to track progress towards, and certify, the achievement of malaria elimination targets.

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Project description:BackgroundInternet search query trends have been shown to correlate with incidence trends for select infectious diseases and countries. Herein, the first use of Google search queries for malaria surveillance is investigated. The research focuses on Thailand where real-time malaria surveillance is crucial as malaria is re-emerging and developing resistance to pharmaceuticals in the region.MethodsOfficial Thai malaria case data was acquired from the World Health Organization (WHO) from 2005 to 2009. Using Google correlate, an openly available online tool, and by surveying Thai physicians, search queries potentially related to malaria prevalence were identified. Four linear regression models were built from different sub-sets of malaria-related queries to be used in future predictions. The models' accuracies were evaluated by their ability to predict the malaria outbreak in 2009, their correlation with the entire available malaria case data, and by Akaike information criterion (AIC).ResultsEach model captured the bulk of the variability in officially reported malaria incidence. Correlation in the validation set ranged from 0.75 to 0.92 and AIC values ranged from 808 to 586 for the models. While models using malaria-related and general health terms were successful, one model using only microscopy-related terms obtained equally high correlations to malaria case data trends. The model built strictly of queries provided by Thai physicians was the only one that consistently captured the well-documented second seasonal malaria peak in Thailand.ConclusionsModels built from Google search queries were able to adequately estimate malaria activity trends in Thailand, from 2005-2010, according to official malaria case counts reported by WHO. While presenting their own limitations, these search queries may be valid real-time indicators of malaria incidence in the population, as correlations were on par with those of related studies for other infectious diseases. Additionally, this methodology provides a cost-effective description of malaria prevalence that can act as a complement to traditional public health surveillance. This and future studies will continue to identify ways to leverage web-based data to improve public health.

Dataset Information

A nowcasting framework for correcting for reporting delays in malaria surveillance.

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A nowcasting framework for correcting for reporting delays in malaria surveillance.

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