Physics

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.

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.

Authors: K. Laneri*, B. Cabella*, P.I. Prado, R.M. Coutinho**, R.A. Kraenkel I'll quickly introduce the Convergent Cross-Mapping (CCM) criterion to investigate causality between two time series. Than I'll present an analysis of the potential environmental drivers of malaria cases in Northwestern Argentina, and discuss plausible interpretations of these results. We have inspected causal links between malaria and climatic variables, based on 12 years of weekly malaria /P. vivax/ cases in Tartagal, Salta, Argentina—at the southern fringe of malaria incidence in the Americas—together with humidity and temperature time-series spanning the same period. Our results show that there are causal links between malaria cases and both maximum temperature, with a delay of five weeks, and minimum temperature, with delays of zero and twenty two weeks. Humidity is also a driver of malaria cases, with thirteen weeks delay between cause and effect. Furthermore we also determined the sign and strength of the effects. These results might be signaling processes operating at short (below 5 weeks) and long (over 12 weeks) time delays, corresponding to effects related to parasite cycle and mosquito population dynamics respectively. The non-linearities found for the strength of the effect of temperature on malaria cases make warmer areas more prone to higher increases in the disease incidence. Moreover, our results indicate that an increase of extreme weather events could enhance the risks of malaria spreading and re-emergence beyond the current distribution. Both situations, warmer climate and increase of extreme events, will be remarkably increased by the end of the century in thishot spot of climate change. * joint first authors** speaker 

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.

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 this work we analyse results obtained when considering individuals as mobile agents, which can interact for a period of time that depends on the agent dinamics. In this way, as the probability of disease transmission depends on this contact time, the spatial dynamics will strongly influence the disease spreading. Surprisingly, the size of the endemic population will have three different regimes depending on the speed: elimination of the disease if it is very low, inversely proportional to the speed for intermediate values, and a final case that depends on the contact. Moreover, we recently found that these last two regimes also depend heavily on the type of the dynamics (ballistic or diffusive). The same goes for the critical speed at which the disease disappears.

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.