Who is Tweeting about COVID-19

During the COVID-19 pandemic many people used social media to seek information about the disease. In addition, these platforms were used to share news and opinions on issues related to COVID-19, such as vaccines and isolation policies. Extracting useful information for public health from these platforms efficiently poses some challenges due to the characteristics of social networks. Therefore, this article presents a method combining clustering and natural language processing for extracting information about users and their posts on social networks about COVID-19. The results show that the combination of clustering methods and textual analysis can reveal valuable information for public health using data from social networks. Future studies can employ this proposed method in order to get real-time information during future epidemics © 2022 IEEE.

Critical Success Factors in Combating Dengue and COVID-19: Lessons for Future Pandemics

The COVID-19 pandemic has affected the entire world, causing millions of deaths. In addition to this disease, many countries also periodically face outbreaks of other diseases, such as dengue. Although the two diseases have their specific characteristics, there may be common factors affecting them. Knowing these factors is essential for governments to plan actions to mitigate the impacts of future epidemics. This research aims to analyze data from several dimensions to identify the critical success factors for the fight against dengue and COVID-19. For this, Data Science techniques were applied to data from 645 cities in the State of São Paulo, Brazil. The results provide important information that may explain why some locations have been more successful than others in fighting those diseases, as well as identifying the common factors that may also impact other diseases. © 2022 IEEE.

Coronavirus from cities to forests: mapping vulnerable interfaces and hotspots for SARS-CoV-2 spillover from humans to biodiversity

Background: With the continuous spreading of SARS-CoV-2 globally, the probability for interactions between humans who are infected and wildlife tends to grow intensely, as well as the likelihood of viral spillover from humans to biodiversity. This aspect is of great concern for wildlife conservation and human health, because the list of highly susceptible animal groups that have contracted SARS-CoV-2 (bats, mustelids, and primates) is large and, once infected, these groups can act as vectors and reservoirs, becoming a substrate for viral mutations and recombinations and boosting the risk of new strains emerging, which can return to humans as new diseases. Little is known about the inducing factors facilitating coronavirus spillover from one species to another, but it can be argued that interface zones between wild fauna and humans, which are narrow edges between anthropic (cities, roads, parks, ecotourism sites, and agricultural frontiers) and sylvatic habitat, are zones of increased interaction between humans and wild animals, and thus have a higher probability of viral spillover events than other areas. In a similar context, the habitat compression by forest fragmentation also brings species and infected beings closer, reducing their home ranges and intensifying the risk of spillover among wild populations. Therefore, on the basis of the premise for zoonosis—the greater human–animal interaction, the greater risk of viral spillover—we aimed to identify the most and least susceptible areas to viral spillover in Brazil. Methods: We developed an approach combining ecological modelling (Biomod2: modelling habitat suitability for 158 bat and 49 primate species) and geographical information systems (by using demographic indicators, roads, and related variables) to map the most and least susceptible areas to spillover in Brazil. This map indicates priority areas for serological surveillance of fauna for monitoring the spillover and circulation of SARS-CoV-2 strains and variants in Brazilian biodiversity. Findings: Among our most relevant preliminary results, we found that forested areas surrounding the São Paulo Metropolitan Area are among the most susceptible areas for spillover. This resulted from the combination of high contaminated human density and high density of non-human primates interacting with humans in these transitional areas. Interpretation: Because of the high resolution of the results, the map can be useful for action planning and decision making in conservation and health, since susceptible areas denote not only a greater risk of virus jumping from humans to animals, but also of coronaviruses returning from fauna to humans in new viral strains. Funding: Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP;2019/12988-7 and 2018/14389-0).