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SUMMARYBackgroundVenezuela has experienced an explosive resurgence in Plasmodium falciparum and Plasmodium vivax malaria incidence over the last decade due to various social, political, and economic factors. To ensure national and regional progress towards malaria elimination, there is an urgent need to better understand the epidemiological dynamics of this malaria outbreak at its epicenter in the southern state of Bolívar and to identify the sets of interventions that may be necessary to reduce transmission and incidence.MethodsWe fitted transmission models of P. falciparum and P. vivax to weekly incidence data in Bolívar, Venezuela during 2000-2018. We estimated the magnitude of local transmission for both Plasmodium spp. and inferred the contribution of relapses and reinfections to P. vivax incidence in the region. Compared to a business-as-usual scenario, we projected the impact of different interventions on Plasmodium spp. incidence during the period 2021-2023.FindingsWe estimated that 63·7 – 73·3% of all P. vivax infections in Bolívar are relapses, leading to as many as 51,800 observed relapses misclassified per year as reinfections in the routine surveillance data. Our estimates suggest that the reproduction number remains close to one for both Plasmodium spp., pointing towards the feasibility of control. Long-lasting insecticidal nets (LLINs) were projected to cause greater proportional reductions in P. falciparum incidence than P. vivax incidence, and mass drug administration (MDA) with an 8-aminoquinoline and a blood-stage partner drug was projected to cause the greatest reduction in P. vivax incidence, provided that adherence rates were high.InterpretationControl of the malaria outbreak in Southeastern Venezuela is feasible, should appropriate resources to support surveillance and control be brought to bear. Coupling the distribution of LLINs and a focal MDA with an 8-aminoquinoline and a blood-stage partner drug may lead to the greatest reduction in malaria incidence.FundingNational Science Foundation; University of Notre Dame; National Institute of General Medical Sciences (grant number 1R35GM143029-01 to TAP);RESEARCH IN CONTEXTEvidence before the studyWe searched PubMed, bioRxiv, and medRxiv for articles in English published on or before May 25th, 2021 using the following keywords: “Venezuela”, “malaria”, AND “model*”. Previous studies have applied statistical models to characterize the relationship between malaria incidence and climate in Venezuela, concluding that the reproduction number is low and suggesting the feasibility of control. A study fitting a mechanistic transmission model to epidemiological data to allow for projecting the impact of alternative approaches to control has not been performed.Added value of the studyWe fitted Plasmodium falciparum and Plasmodium vivax transmission models to 20 years of weekly incidence data to estimate the transmission of both Plasmodium spp. and characterize the contribution of relapses and reinfections to P. vivax incidence in Bolívar, Venezuela. We also projected the likely impact of interventions in the region under alternative scenarios about control.Implications of the available evidenceThe burden of Plasmodium vivax relapses in Bolívar is underestimated from routine surveillance data, so control interventions must target the hypnozoite reservoir in the region. Mass drug administration (MDA) is projected to be impactful for both Plasmodium spp., though tradeoffs between coverage and adherence suggest that a focal MDA with an 8-aminoquinoline and a blood-stage partner drug may yield the greatest impact.

When new pathogens emerge, numerous questions arise about their future spread, some of which can be addressed with probabilistic forecasts. The many uncertainties about the epidemiology of emerging pathogens can make it difficult to choose among model structures and assumptions, however. To assess the potential for uncertainties about emerging pathogens to affect forecasts of their spread, we evaluated the performance of a suite of 16 forecasting models in the context of the 2015-2016 Zika epidemic in Colombia. Each model featured a different combination of assumptions about the role of human mobility in driving transmission, spatiotemporal variation in transmission potential, and the number of times the virus was introduced. All models used the same core transmission model and the same iterative data assimilation algorithm to generate forecasts. By assessing forecast performance through time using logarithmic scoring with ensemble weighting, we found that which model assumptions had the most ensemble weight changed through time. In particular, spatially coupled models had higher ensemble weights in the early and late phases of the epidemic, whereas non-spatial models had higher ensemble weights at the peak of the epidemic. We compared forecast performance of the equally-weighted ensemble model to each individual model and identified a trade-off whereby certain individual models outperformed the ensemble model early in the epidemic but the ensemble model outperformed all individual models on average. On balance, our results suggest that suites of models that span uncertainty across alternative assumptions are necessary to obtain robust forecasts in the context of emerging infectious diseases.

Predicting an infectious disease can help reduce its impact by advising public health interventions and personal preventive measures. While availability of heterogeneous data streams and sensors such as satellite imagery and the Internet have increased the opportunity to indirectly measure, understand, and predict global dynamics, the data may be prohibitively large and/or require intensive data management while also requiring subject matter experts to properly exploit the data sources (e.g., deriving features from fundamentally different data sets). Few efforts have quantitatively assessed the predictive benefit of novel data streams in comparison to more traditional data sources, especially at fine spatio-temporal resolutions. We have combined multiple traditional and non-traditional data streams (satellite imagery, Internet, weather, census, and clinical surveillance data) and assessed their combined ability to predict dengue in Brazil’s 27 states on a weekly and yearly basis over seven years. For each state, we nowcast dengue based on several time series models, which vary in complexity and inclusion of exogenous data. We also predict yearly cumulative risk by municipality and state. The top-performing model and utility of predictive data varies by state, implying that forecasting and nowcasting efforts in the future may be made more robust by and benefit from the use of multiple data streams and models. One size does not fit all, particularly when considering state-level predictions as opposed to the whole country. Our first-of-its-kind high resolution flexible system for predicting dengue incidence with heterogeneous (and still sometimes sparse) data can be extended to multiple applications and regions.

Since the first Zika virus (ZIKV) infection was confirmed in Brazil in May 2015, several hundred thousand cases have been reported across the Americas. This figure gives an incomplete picture of the epidemic, however, given that asymptomatic infections, imperfect surveillance, and variability in reporting rates imply that the incidence of infection was likely much higher. The infection attack rate (IAR)—defined as the proportion of the population that was infected over the course of the epidemic—has important implications for the longer-term epidemiology of Zika in the region, such as the timing, location, and likelihood of future outbreaks. To estimate the IAR and the total number of people infected, we leveraged multiple types of Zika case data from 15 countries and territories where subnational data were publicly available. Datasets included confirmed and suspected Zika cases in pregnant women and in the total population, Zika-associated Guillan-Barré syndrome cases, and cases of congenital Zika syndrome. We used a hierarchical Bayesian model with empirically-informed priors that leveraged the different case report types to simultaneously estimate national and subnational reporting rates, the fraction of symptomatic infections, and subnational IARs. In these 15 countries and territories, estimates of Zika IAR ranged from 0.084 (95% CrI: 0.067 − 0.096) in Peru to 0.361 (95% CrI: 0.214 − 0.514) in Ecuador, with significant subnational variability in IAR for every country. Totaling these infection estimates across these and 33 other countries and territories in the region, our results suggest that 132.3 million (95% CrI: 111.3-170.2 million) people in the Americas have been infected by ZIKV since 2015. These estimates represent the most extensive attempt to date to determine the size of the Zika epidemic in the Americas, and they offer an important baseline for assessing the risk of future Zika epidemics in this region.

Background: Temporal incidence patterns provide a crucial window into the dynamics of emerging infectious diseases, yet their utility is limited by the spatially aggregated form in which they are often presented. Weekly incidence data from the 2015-2016 Zika epidemic were available only at the national level for most countries in the Americas. One exception was Colombia, where data at departmental and municipal scales were made publicly available in real time, providing an opportunity to assess the degree to which national-level data are reflective of temporal patterns at local levels. Methods: To characterize differences in epidemic trajectories, our analysis centered on classifying proportional cumulative incidence curves according to six features at three levels of spatial aggregation. This analysis used the partitioning around medoids algorithm to assign departments and municipalities to groups based on these six characteristics. Examination of the features that differentiated these groups and exploration of their temporal and spatial patterns were performed. Simulations from a stochastic transmission model provided data that were used to assess the extent to which groups identified by the classification algorithm could be associated with differences in underlying drivers of transmission. Results: The timing of departmental-level epidemic peaks varied by three months, and departmental-level estimates of the time-varying reproduction number, R(t), showed patterns that were distinct from a national-level estimate. The classification algorithm identified moderate support for two to three clusters at the departmental level and somewhat stronger support for this at the municipal level. Variability in epidemic duration, the length of the tail of the epidemic, and the consistency of cumulative incidence data with a cumulative normal distribution function made the greatest contributions to distinctions across these groups. Applying the classification algorithm to simulated data showed that municipalities with basic reproduction number, R0, greater than 1 were consistently associated with a particular group. Municipalities with R0 < 1 displayed more diverse patterns, although in this case that may be due to simplifications of how the model represented spatial interaction among municipalities. Conclusions: The diversity of temporal incidence patterns at local scales uncovered by this analysis underscores the value of spatially disaggregated data and the importance of locally tailored strategies for responding to emerging infectious diseases.

Numerous Zika virus vaccines are being developed. However, identifying sites to evaluate the efficacy of a Zika virus vaccine is challenging due to the general decrease in Zika virus activity. We compare results from three different modeling approaches to estimate areas that may have increased relative risk of Zika virus transmission during 2017. The analysis focused on eight priority countries (i.e., Brazil, Colombia, Costa Rica, Dominican Republic, Ecuador, Mexico, Panama, and Peru). The models projected low incidence rates during 2017 for all locations in the priority countries but identified several subnational areas that may have increased relative risk of Zika virus transmission in 2017. Given the projected low incidence of disease, the total number of participants, number of study sites, or duration of study follow-up may need to be increased to meet the efficacy study endpoints.