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

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

Sequestration and Tissue Accumulation of Human Malaria Parasites: Can We Learn Anything from Rodent Models of Malaria?

The sequestration of Plasmodium falciparum–infected red blood cells (irbcs) in the microvasculature of organs is associated with severe disease; correspondingly, the molecular basis of irbc adherence is an active area of study. In contrast to P. falciparum, much less is known about sequestration in other Plasmodium parasites, including those species that are used as models to study severe malaria. Here, we review the cytoadherence properties of irbcs of the rodent parasite Plasmodium berghei ANKA, where schizonts demonstrate a clear sequestration phenotype. Real-time in vivo imaging of transgenic P. berghei parasites in rodents has revealed a CD36-dependent sequestration in lungs and adipose tissue. In the absence of direct orthologs of the P. falciparum proteins that mediate binding to human CD36, the P. berghei proteins and/or mechanisms of rodent CD36 binding are as yet unknown. In addition to CD36-dependent schizont sequestration, irbcs accumulate during severe disease in different tissues, including the brain. The role of sequestration is discussed in the context of disease as are the general (dis)similarities of P. berghei and P. falciparum sequestration

Integrating NIMH Research Domain Criteria (RDoC) into PTSD Research

Three and a half decades of research on posttraumatic stress disorder (PTSD) has produced substantial knowledge on the pathobiology of this frequent and debilitating disease. However, despite all research efforts, so far no drug that has specifically targeted PTSD core symptoms progressed to clinical use. Instead, although not overly efficient, serotonin re-uptake inhibitors continue to be considered the gold standard of PTSD pharmacotherapy. The psychotherapeutic treatment and symptom-oriented drug therapy options available for PTSD treatment today show some efficacy, although not in all PTSD patients, in particular not in a substantial percent of those suffering from the detrimental sequelae of repeated childhood trauma or in veterans with combat related PTSD. PTSD has this in common with other psychiatric disorders - in particular effective treatment for incapacitating conditions such as resistant major depression, chronic schizophrenia, and frequently relapsing obsessive-compulsive disorder as well as dementia has not yet been developed through modern neuropsychiatric research.In response to this conundrum, the National Institute of Mental Health launched the Research Domain Criteria (RDoC) framework which aims to leave diagnosis-oriented psychiatric research behind and to move on to the use of research domains overarching the traditional diagnosis systems. To the best of our knowledge, the paper at hand is the first that has systematically assessed the utility of the RDoC system for PTSD research. Here, we review core findings in neurobiological PTSD research and match them to the RDoC research domains and units of analysis. Our synthesis reveals that several core findings in PTSD such as amygdala overactivity have been linked to all RDoC domains without further specification of their distinct role in the pathophysiological pathways associated with these domains. This circumstance indicates that the elucidation of the cellular and molecular processes ultimately decisive for regulation of psychic processes and for the expression of psychopathological symptoms is still grossly incomplete. All in all, we find the RDoC research domains to be useful but not sufficient for PTSD research. Hence, we suggest adding two novel domains, namely stress and emotional regulation and maintenance of consciousness. As both of these domains play a role in various if not in all psychiatric diseases, we judge them to be useful not only for PTSD research but also for psychiatric research in general