Physics

Mosquito-borne diseases have been emerging and re-emerging in the Americas, causing millions of human illnesses. Recent examples include Zika virus and chikungunya. In order to quantify the impact of past outbreaks and predict the course of future outbreaks, it is necessary to merge models with heterogeneous data streams. We present both statistical and mechanistic modeling for mosquito-borne disease spread coupled with data including demographics, internet data, human case counts, weather, and satellite to predict risk. Wehighlight the relative usefulness of our data streams and models depending on the question we are answering and its scale.

Zoonotic Visceral Leishmaniasis (ZVL) is one of the world's deadliest and neglected infectious diseases, according to World Health Organization. In this sense, it is important to optimize the control strategy activities. In this work, it was evaluated five control strategies (positive dog elimination, insecticide impregnated dog collar, dog vaccination, dog treatment, and sandfly population control), considering disease control results and cost-effectiveness. It was elaborated a mathematical model based on a set of differential equations in which three populations were represented (human, dog, and sandfly). Humans and dogs were divided into susceptible, latent, clinically ill, and recovery categories. Sandflies were divided into noninfected, infected, and infective. According to our model, the insecticide impregnated dog collar was the strategy that presented the best combination between disease control and cost-effectiveness.

Brazil accounts for ~80% of dengue (DENV) cases in the Americas. DENV outbreaks vary spatially within a season and temporally across seasons due to cocirculating serotypes, coexistence of competent vectors, and five distinct climatic zones, making prediction efforts challenging. Identifying specific environmental and sociodemographic factors that explain this variation is key to improving such prediction efforts.  Ecological proxy data (i.e., indirect data sources)  can be used to capture the complex interactions between the host, pathogen, and environment and provide information for local conditions in the absence of granular statistics collected on the ground. Here, we use a combination of 36 satellite imagery, weather, clinical, and census data streams to characterize DENV outbreak shape, seasonal timing, and pairwise correlations in magnitude and onset at the Brazilian state (N=27) and meso region (N=137) levels. Using cross-validated regularized regression models to analyze weekly DENV case data from 2010-2016, we find a parsimonious set of ecological data that explains each outbreak property and measure how well each model captures the property’s variation through several performance metrics. From 2010-2016, the seasonal onset in Para (northern region) was on average 6.74 weeks ahead of other states, positioning it as a potential sentinel state. At the state level, our models explained 62.8% of the variation in outbreak shape, 51.1% of pairwise correlation in outbreak timing,  48.5% of seasonal onset, and 13.0% of pairwise correlation in outbreak magnitude. Outbreak properties were generally better explained at the state level rather than the meso region level. The normalized burn ratio (NBR) had the strongest effect on outbreak shape, while the mean daily temperature range most impacted the state seasonal onset.  The pairwise correlation in outbreak timing between states was best predicted by distance, while the pairwise correlation in outbreak magnitude was best predicted by the similarity in population density. Overall, our results highlight the utility of diverse and disparate ecological data streams for understanding the complex mechanisms that drive DENV transmission dynamics across geographic regions, and how this knowledge can be used to improve the formulation of spatially linked forecasting models of DENV activity.

Healthcare-associated infections cause significant patient morbidity and mortality, and contribute to growinghealthcare costs, whose effects may be felt most strongly in developing countries. Active surveillance systems,hospital staff compliance, including hand hygiene, and a rational use of antimicrobials are among the importantmeasures to mitigate the spread of healthcare-associated infection within and between hospitals. Klebsiellapneumoniae is an important human pathogen that can spread in hospital settings, with some forms exhibitingdrug resistance, including resistance to the carbapenem class of antibiotics, the drugs of last resort for suchinfections. Focusing on the role of patient movement within and between hospitals on the transmission andincidence of enterobacteriaceae producing K. pneumoniae Carbapenemase (KPC, an enzyme that inactivatesseveral antimicrobials), we developed a metapopulation model where the connections among hospitals are madeusing a theoretical hospital network based on Brazilian hospital sizes and locations. The pathogen reproductivenumber, R_0 that measures the average number of new infections caused by a single infectious individual, wascalculated in different scenarios defined by both the links between hospital environments (regular wards andintensive care units) and between different hospitals (patient transfer). Numerical simulation was used to il-lustrate the infection dynamics in this set of scenarios. The sensitivity of R_0 to model input parameters, such ashospital connectivity and patient-hospital staff contact rates was also established, highlighting the differentialimportance of factors amenable to change on pathogen transmission and control.

Quantitative methods are unquestionably valuable tools for disease propagation models, and have trackedattention of mathematicians, physicists, and statisticians for decades. Mathematical models for diseasepropagation have become more and more sophisticated, incorporating different sets of heterogeneities inthe hope to be as accurate as possible while still being mathematically and computationally tractable. Sociodemographic characteristics such as age distribution, study and work activities, and local and long-range mobility, have been key to study historical infection data and devise scenario assessment. Recent outbreaks of global concern such as the influenza H1N1pdm09, SARS, MERS-CoV, Ebola, Zika, and thecurrent COVID-19 have tested our ability to produce information that is fundamental for preparedness:reproductive number estimation, importation case probability, short-term predictions of disease evolution,and so on. On the other hand, endemic diseases ask for a complementary approach to disease propagationmodels, which is that of disease surveillance. How do we properly separate baseline activity,characterized by stochastic fluctuations and isolated case bursts, from epidemic activity? In other words,how many weekly cases we need to say that the current season of “disease A” have started? Is that levelthe same everywhere? Does influenza-like illnesses always start in the winter? As for real-timesurveillance, are the available data delivered in a timely manner or is there significant delay between caseoccurrence and official report? In this talk we will go over both approaches highlighting the nuances ofeach, their importance to provide relevant information to public health actions, and, hopefully, spark yourinterest in joining the field.

There is no “one size fits all” intervention for malaria elimination due to the spectrum of available sub-optimal interventions acting at different stages of the parasite life-cycle and the heterogeneous transmission landscape. Every district of every country has its own unique challenges, conditions and solutions. Mathematical modelling is the best available approach for combining the many interacting factors that must be considered. In a recent project bespoke mathematical and economic models were developed in parallel with training a new group of modellers based in their own countries around the World. The resulting research demonstrates that modelling can be used to support all aspects of the World Health Organization Global Technical Strategy for Malaria within the countries where the disease persists.

The overall malaria burden in the Americas has decreased dramatically over the past two decades, but residual transmission pockets persist across the Amazon Basin, where Plasmodium vivax is the predominant infecting species. Current elimination efforts require a better quantitative understanding of malaria transmission dynamics for planning, monitoring, and evaluating interventions at the community level. This can be achieved with mathematical models that properly account for risk heterogeneity in communities approaching elimination, where few individuals disproportionately contribute to overall malaria prevalence, morbidity, and onwards transmission. Here we analyse demographic information combined with routinely collected malaria morbidity data from the town of Mâncio Lima, the main urban transmission hotspot of Brazil. We estimate the proportion of high-risk subjects in the host population by fitting compartmental susceptible-infected-susceptible (SIS) transmission models simultaneously to age-stratified vivax malaria incidence densities and the frequency distribution of P. vivax malaria attacks experienced by each individual over 12 months. Simulations with the best-fitting SIS model indicate that 20% of the hosts contribute 86% of the overall vivax malaria burden. Despite the low overall force of infection typically found in the Amazon, about one order of magnitude lower than that in rural Africa, high-risk individuals gradually develop clinical immunity following repeated infections and eventually constitute a substantial infectious reservoir comprised of asymptomatic parasite carriers that is overlooked by routine surveillance but likely fuels onwards malaria transmission. High-risk individuals therefore represent a priority target for more intensive and effective interventions that may not be readily delivered to the entire community.