Dengue is a mosquito-transmitted virus infection that causes epidemics of febrile illness and hemorrhagic fever across the tropics and subtropics worldwide. Annual epidemics are commonly observed, but there is substantial spatiotemporal heterogeneity in intensity. A better understanding of this heterogeneity in dengue transmission could lead to improved epidemic prediction and disease control. Time series decomposition methods enable the isolation and study of temporal epidemic dynamics with a specific periodicity (e.g., annual cycles related to climatic drivers and multiannual cycles caused by dynamics in population immunity). We collected and analyzed up to 18 y of monthly dengue surveillance reports on a total of 3.5 million reported dengue cases from 273 provinces in eight countries in Southeast Asia, covering ∼10 7 km 2 . We detected strong patterns of synchronous dengue transmission across the entire region, most markedly during a period of high incidence in 1997-1998, which was followed by a period of extremely low incidence in 2001-2002. This synchrony in dengue incidence coincided with elevated temperatures throughout the region in 1997-1998 and the strongest El Niño episode of the century. Multiannual dengue cycles (2-5 y) were highly coherent with the Oceanic Niño Index, and synchrony of these cycles increased with temperature. We also detected localized traveling waves of multiannual dengue epidemic cycles in Thailand, Laos, and the Philippines that were dependent on temperature. This study reveals forcing mechanisms that drive synchronization of dengue epidemics on a continental scale across Southeast Asia.is an arbovirus transmitted by Aedes mosquitos in the tropics and subtropics of the world. The virus causes an estimated 390 million infections per year, resulting in 96 million clinically symptomatic cases (1). DENV has four serotypes (DENV-1, DENV-2, DENV-3, and DENV-4) that each circulate worldwide. The spatial propagation of dengue transmission at short distances by the mosquito vector is well-understood, but the mechanism of long-distance spread has remained unclear. Disease transmission over large geographical distances is difficult to measure directly, but epidemiological coupling of locations revealed by synchrony in population-level disease patterns has been used successfully in the past to infer mechanisms of spread
BackgroundChikungunya virus (CHIKV) is a globally re-emerging arbovirus for which previous studies have indicated the majority of infections result in symptomatic febrile illness. We sought to characterize the proportion of subclinical and symptomatic CHIKV infections in a prospective cohort study in a country with known CHIKV circulation.Methods/FindingsA prospective longitudinal cohort of subjects ≥6 months old underwent community-based active surveillance for acute febrile illness in Cebu City, Philippines from 2012-13. Subjects with fever history were clinically evaluated at acute, 2, 5, and 8 day visits, and at a 3-week convalescent visit. Blood was collected at the acute and 3-week convalescent visits. Symptomatic CHIKV infections were identified by positive CHIKV PCR in acute blood samples and/or CHIKV IgM/IgG ELISA seroconversion in paired acute/convalescent samples. Enrollment and 12-month blood samples underwent plaque reduction neutralization test (PRNT) using CHIKV attenuated strain 181/clone25. Subclinical CHIKV infections were identified by ≥8-fold rise from a baseline enrollment PRNT titer <10 without symptomatic infection detected during the intervening surveillance period. Selected CHIKV PCR-positive samples underwent viral isolation and envelope protein-1 gene sequencing. Of 853 subjects who completed all study procedures at 12 months, 19 symptomatic infections (2.19 per 100 person-years) and 87 subclinical infections (10.03 per 100 person-years) occurred. The ratio of subclinical-to-symptomatic infections was 4.6:1 varying with age from 2:1 in 6 month-5 year olds to 12:1 in those >50 years old. Baseline CHIKV PRNT titer ≥10 was associated with 100% (95%CI: 46.1, 100.0) protection from symptomatic CHIKV infection. Phylogenetic analysis demonstrated Asian genotype closely related to strains from Asia and the Caribbean.ConclusionsSubclinical infections accounted for a majority of total CHIKV infections. A positive baseline CHIKV PRNT titer was associated with protection from symptomatic CHIKV infection. These findings have implications for assessing disease burden, understanding virus transmission, and supporting vaccine development.
Proper understanding of the long-term epidemiology of chikungunya has been hampered by poor surveillance. Outbreak years are unpredictable and cases often misdiagnosed. Here we analyzed age-specific data from 2 serological studies (from 1973 and 2012) in Cebu, Philippines, to reconstruct both the annual probability of infection and population-level immunity over a 60-year period (1952–2012). We also explored whether seroconversions during 2012–2013 were spatially clustered. Our models identified 4 discrete outbreaks separated by an average delay of 17 years. On average, 23% (95% confidence interval [CI], 16%–37%) of the susceptible population was infected per outbreak, with >50% of the entire population remaining susceptible at any point. Participants who seroconverted during 2012–2013 were clustered at distances of <230 m, suggesting focal transmission. Large-scale outbreaks of chikungunya did not result in sustained multiyear transmission. Nevertheless, we estimate that >350 000 infections were missed by surveillance systems. Serological studies could supplement surveillance to provide important insights on pathogen circulation.
BackgroundAccurate prediction of dengue incidence levels weeks in advance of an outbreak may reduce the morbidity and mortality associated with this neglected disease. Therefore, models were developed to predict high and low dengue incidence in order to provide timely forewarnings in the Philippines.MethodsModel inputs were chosen based on studies indicating variables that may impact dengue incidence. The method first uses Fuzzy Association Rule Mining techniques to extract association rules from these historical epidemiological, environmental, and socio-economic data, as well as climate data indicating future weather patterns. Selection criteria were used to choose a subset of these rules for a classifier, thereby generating a Prediction Model. The models predicted high or low incidence of dengue in a Philippines province four weeks in advance. The threshold between high and low was determined relative to historical incidence data.Principal FindingsModel accuracy is described by Positive Predictive Value (PPV), Negative Predictive Value (NPV), Sensitivity, and Specificity computed on test data not previously used to develop the model. Selecting a model using the F0.5 measure, which gives PPV more importance than Sensitivity, gave these results: PPV = 0.780, NPV = 0.938, Sensitivity = 0.547, Specificity = 0.978. Using the F3 measure, which gives Sensitivity more importance than PPV, the selected model had PPV = 0.778, NPV = 0.948, Sensitivity = 0.627, Specificity = 0.974. The decision as to which model has greater utility depends on how the predictions will be used in a particular situation.ConclusionsThis method builds prediction models for future dengue incidence in the Philippines and is capable of being modified for use in different situations; for diseases other than dengue; and for regions beyond the Philippines. The Philippines dengue prediction models predicted high or low incidence of dengue four weeks in advance of an outbreak with high accuracy, as measured by PPV, NPV, Sensitivity, and Specificity.
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