Covid-19 Dynamic Monitoring and Real-Time Spatio-Temporal Forecasting

Background: Periodically, humanity is often faced with new and emerging viruses that can be a significant global threat. It has already been over a century post—the Spanish Flu pandemic, and we are witnessing a new type of coronavirus, the SARS-CoV-2, which is responsible for Covid-19. It emerged from the city of Wuhan (China) in December 2019, and within a few months, the virus propagated itself globally now resulting more than 50 million cases with over 1 million deaths. The high infection rates coupled with dynamic population movement demands for tools, especially within a Brazilian context, that will support health managers to develop policies for controlling and combating the new virus. / Methods: In this work, we propose a tool for real-time spatio-temporal analysis using a machine learning approach. The COVID-SGIS system brings together routinely collected health data on Covid-19 distributed across public health systems in Brazil, as well as taking to under consideration the geographic and time-dependent features of Covid-19 so as to make spatio-temporal predictions. The data are sub-divided by federative unit and municipality. In our case study, we made spatio-temporal predictions of the distribution of cases and deaths in Brazil and in each federative unit. Four regression methods were investigated: linear regression, support vector machines (polynomial kernels and RBF), multilayer perceptrons, and random forests. We use the percentage RMSE and the correlation coefficient as quality metrics. / Results: For qualitative evaluation, we made spatio-temporal predictions for the period from 25 to 27 May 2020. Considering qualitatively and quantitatively the case of the State of Pernambuco and Brazil as a whole, linear regression presented the best prediction results (thematic maps with good data distribution, correlation coefficient >0.99 and RMSE (%) <4% for Pernambuco and around 5% for Brazil) with low training time: [0.00; 0.04 ms], CI 95%. / Conclusion: Spatio-temporal analysis provided a broader assessment of those in the regions where the accumulated confirmed cases of Covid-19 were concentrated. It was possible to differentiate in the thematic maps the regions with the highest concentration of cases from the regions with low concentration and regions in the transition range. This approach is fundamental to support health managers and epidemiologists to elaborate policies and plans to control the Covid-19 pandemics

COVID-SGIS: A Smart Tool for Dynamic Monitoring and Temporal Forecasting of Covid-19

Background: The global burden of the new coronavirus SARS-CoV-2 is increasing at an unprecedented rate. The current spread of Covid-19 in Brazil is problematic causing a huge public health burden to its population and national health-care service. To evaluate strategies for alleviating such problems, it is necessary to forecast the number of cases and deaths in order to aid the stakeholders in the process of making decisions against the disease. We propose a novel system for real-time forecast of the cumulative cases of Covid-19 in Brazil. / Methods: We developed the novel COVID-SGIS application for the real-time surveillance, forecast and spatial visualization of Covid-19 for Brazil. This system captures routinely reported Covid-19 information from 27 federative units from the Brazil.io database. It utilizes all Covid-19 confirmed case data that have been notified through the National Notification System, from March to May 2020. Time series ARIMA models were integrated for the forecast of cumulative number of Covid-19 cases and deaths. These include 6-days forecasts as graphical outputs for each federative unit in Brazil, separately, with its corresponding 95% CI for statistical significance. In addition, a worst and best scenarios are presented. / Results: The following federative units (out of 27) were flagged by our ARIMA models showing statistically significant increasing temporal patterns of Covid-19 cases during the specified day-to-day period: Bahia, Maranhão, Piauí, Rio Grande do Norte, Amapá, Rondônia, where their day-to-day forecasts were within the 95% CI limits. Equally, the same findings were observed for Espírito Santo, Minas Gerais, Paraná, and Santa Catarina. The overall percentage error between the forecasted values and the actual values varied between 2.56 and 6.50%. For the days when the forecasts fell outside the forecast interval, the percentage errors in relation to the worst case scenario were below 5%. / Conclusion: The proposed method for dynamic forecasting may be used to guide social policies and plan direct interventions in a cost-effective, concise, and robust manner. This novel tools can play an important role for guiding the course of action against the Covid-19 pandemic for Brazil and country neighbors in South America

Feasibility, drug safety, and effectiveness of etiological treatment programs for Chagas disease in Honduras, Guatemala, and Bolivia: 10-year experience of Médecins Sans Frontières

BACKGROUND: Chagas disease (American trypanosomiasis) is a zoonotic or anthropozoonotic disease caused by the parasite Trypanosoma cruzi. Predominantly affecting populations in poor areas of Latin America, medical care for this neglected disease is often lacking. Médecins Sans Frontières/Doctors Without Borders (MSF) has provided diagnostic and treatment services for Chagas disease since 1999. This report describes 10 years of field experience in four MSF programs in Honduras, Guatemala, and Bolivia, focusing on feasibility protocols, safety of drug therapy, and treatment effectiveness. METHODOLOGY: From 1999 to 2008, MSF provided free diagnosis, etiological treatment, and follow-up care for patients <18 years of age seropositive for T. cruzi in Yoro, Honduras (1999-2002); Olopa, Guatemala (2003-2006); Entre Ríos, Bolivia (2002-2006); and Sucre, Bolivia (2005-2008). Essential program components guaranteeing feasibility of implementation were information, education, and communication (IEC) at the community and family level; vector control; health staff training; screening and diagnosis; treatment and compliance, including family-based strategies for early detection of adverse events; and logistics. Chagas disease diagnosis was confirmed by testing blood samples using two different diagnostic tests. T. cruzi-positive patients were treated with benznidazole as first-line treatment, with appropriate counseling, consent, and active participation from parents or guardians for daily administration of the drug, early detection of adverse events, and treatment withdrawal, when necessary. Weekly follow-up was conducted, with adverse events recorded to assess drug safety. Evaluations of serological conversion were carried out to measure treatment effectiveness. Vector control, entomological surveillance, and health education activities were carried out in all projects with close interaction with national and regional programs. RESULTS: Total numbers of children and adolescents tested for T. cruzi in Yoro, Olopa, Entre Ríos, and Sucre were 24,471, 8,927, 7,613, and 19,400, respectively. Of these, 232 (0.9%), 124 (1.4%), 1,475 (19.4%), and 1,145 (5.9%) patients, respectively, were diagnosed as seropositive. Patients were treated with benznidazole, and early findings of seroconversion varied widely between the Central and South American programs: 87.1% and 58.1% at 18 months post-treatment in Yoro and Olopa, respectively; 5.4% by up to 60 months in Entre Ríos; and 0% at an average of 18 months in Sucre. Benznidazole-related adverse events were observed in 50.2% and 50.8% of all patients treated in Yoro and Olopa, respectively, and 25.6% and 37.9% of patients in Entre Ríos and Sucre, respectively. Most adverse events were mild and manageable. No deaths occurred in the treatment population. CONCLUSIONS: These results demonstrate the feasibility of implementing Chagas disease diagnosis and treatment programs in resource-limited settings, including remote rural areas, while addressing the limitations associated with drug-related adverse events. The variability in apparent treatment effectiveness may reflect differences in patient and parasite populations, and illustrates the limitations of current treatments and measures of efficacy. New treatments with improved safety profiles, pediatric formulations of existing and new drugs, and a faster, reliable test of cure are all urgently needed

Perspectives on the Trypanosoma cruzi-host cell receptor interaction

Chagas disease is caused by the parasite Trypanosoma cruzi. The critical initial event is the interaction of the trypomastigote form of the parasite with host receptors. This review highlights recent observations concerning these interactions. Some of the key receptors considered are those for thromboxane, bradykinin, and for the nerve growth factor TrKA. Other important receptors such as galectin-3, thrombospondin, and laminin are also discussed. Investigation into the molecular biology and cell biology of host receptors for T. cruzi may provide novel therapeutic targets