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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.

In this talk, we propose an approach based on machine learning algorithms to predict epidemic processes in complex networks. Specifically, we show that it is possible to estimate the outbreak size starting from a single node in a network and determine which networks properties are the most related to the spreading dynamics. Our approach is general and can be applied to any dynamical process running on top of complex networks. Likewise, our work constitutes an important step towards the application of machine learning methods to unravel dynamical patterns emerging in complex networked systems.

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.

Globalization has created complex problems that can no longer be adequately understood and mitigated using traditional data analysis techniques and data sources. As such, there is a need for the integration of nontraditional data streams and approaches such as social media and machine learning to address these new challenges. In this talk, I will discuss how our team is applying approaches from the weather forecasting community including data collection, assimilating heterogeneous data streams into models, and quantifying uncertainty to forecast influenza and other infectious diseases. In addition, I will demonstrate that although epidemic forecasting is still in its infancy, it’s a growing field with great potential and mathematical modeling will play a key role in making this happen. 

In my presentation I will comment on the preliminary advances and results that we have in the Centro de Estudios de Variabilidad y Cambio Climático (CEVARCAM, FICH-UNL), where since last year a group of researchers is being formed in the line of assessing the impact of climate change on the health of the population. Particularly we study the incidence of infectious diseases and their relationship with different hydroclimatic and environmental indicators in Northeast Argentina. I work in deterministic modelling of infectious diseases while another partner works in stochastic modeling.

Artificial Neural Networks (ANNs) have been demonstrated to be an excellent method for dealing with various kind of tasks, such as image classification and natural language processing. These ANNs can also be used for regression since they are considered to be an universal function approximator, thanks to it's great capacity of dealing with non linear tasks. On the other hand, Floris Takens have shown to be possible to access and reconstruct the underlying dynamics of a system in the space state, starting from a single measured time series. In other words, Takens' theorem says it is possible to get information of a higher dimensional system from vectors build from a one-dimensional time series. This enables better time-series representations that can be fed into the ANNs. In this work, I will apply Echo State Networks (ESNs), one among the types of ANNs, to make forecasts of the dynamics of physical systems, such as Lorenz, Fokker-Planck and Vlasov Equations. I also pretend to apply these ESNs, together with Takens Vectors, into real time series, such as epidemics spread and financial series.

Brazilian Spotted Fever is an infectious, acute febrile disease, of varying severity, whose clinical presentation in humans can vary from mild and atypical forms to severe forms, with a high lethality rate. It is caused by a bacterium of the genus Rickettsia (Rickettsia rickettsii) and transmitted by ticks of the genus Amblyomma. In some locations in the state of São Paulo, the capybara (Hydrochoerus hydrochaeris) participates in the disease cycle as an amplifier of the etiologic agent after contact with infected ticks. As a result of this possible participation of the capybara in the cycle and the eventual need to carry out population management of the species, the State of São Paulo, through the Secretariat of Infraestructure and Environment and the Secretariat of Health, published Resolution SMA-SES No. 01/2016, which provides the -œTechnical guidelines for the surveillance and control of Brazilian Spotted Fever in the State of São Paulo - classification of areas and recommended measures -, which establishes criteria for the management of capybaras in situations where this species is associated with the risk of transmitting Brazilian Spotted Fever to humans.

The prediction of dengue outbreaks based on disease surveillance is  a very hard problem and a lot of work  had been done in this direction. In this talk, we present two studies with different techniques to partially address the problem. First, with the data for the state of São Paulo, we propose a time-scale separation to adjust the parameters and explore some cell phone data. In the second, using the incidence data for the state of Rio de Janeiro, inspired by the work of Saba, Vale, Moret and Miranda, we propose a way to identify a correlation network that would capture the space connections between the outbreaks.