Arbovirus and its potential to lead the next global pandemic from sub-Saharan Africa: What lessons have we learned from COVID-19?

Risk and predisposing factors for viral zoonoses abound in the sub-Saharan Africa (SSA) region with significant public health implications. For several decades, there have been several reports on the emergence and re-emergence of arbovirus infections. The lifetime burden of arboviral diseases in developing countries is still poorly understood. Studies indicate significant healthcare disruptions and economic losses attributed to the viruses in resource-poor communities marked by impairment in the performance of daily activities. Arboviruses have reportedly evolved survival strategies to aid their proliferation in favorable niches, further magnifying their public health relevance. However, there is poor knowledge about the viruses in the region. Thus, this review presents a survey of zoonotic arboviruses in SSA, the burden associated with their diseases, management of diseases as well as their prevention and control, mobility and determinants of infections, their vectors, and co-infection with various microorganisms. Lessons learned from the ongoing coronavirus disease 2019 (COVID-19) pandemic coupled with routine surveillance of zoonotic hosts for these viruses will improve our understanding of their evolution, their potential to cause a pandemic, control and prevention measures, and vaccine development.Copyright © GERMS 2022.

Quantitative assessment of the effects of resource optimization and ICU admission policy on COVID-19 mortalities

It is evident that increasing the intensive-care-unit (ICU) capacity and giving priority to admitting and treating patients will reduce the number of COVID-19 deaths, but the quantitative assessment of these measures has remained inadequate. We develop a comprehensive, non-Markovian state transition model, which is validated through the accurate prediction of the daily death toll for two epicenters: Wuhan, China and Lombardy, Italy. The model enables prediction of COVID-19 deaths in various scenarios. For example, if appropriate treatment priorities had been used, the death toll in Wuhan and Lombardy would have been reduced by about 10% and 7%, respectively. The strategy depends on the epidemic scale and is more effective in countries with a younger population structure. Analyses of data from China, South Korea, Italy, and Spain suggest that countries with less per capita ICU medical resources should implement this strategy in the early stage of the pandemic to reduce mortalities. We emphasize that the results of this paper should be interpreted purely from a scientific and a quantitative-analysis point of view. No ethical implications are intended and meaningful. © 2022 authors. Published by the American Physical Society. Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

Characterizing Covid Waves via Spatio-Temporal Decomposition

In this paper we develop a framework for analyzing patterns of a disease or pandemic such as Covid. Given a dataset which records information about the spread of a disease over a set of locations, we consider the problem of identifying both the disease's intrinsic waves (temporal patterns) and their respective spatial epicenters. To do so we introduce a new method of spatio-temporal decomposition which we call diffusion NMF (D-NMF). Building upon classic matrix factorization methods, D-NMF takes into consideration a spatial structuring of locations (features) in the data and supports the idea that locations which are spatially close are more likely to experience the same set of waves. To illustrate the use of D-NMF, we analyze Covid case data at various spatial granularities. Our results demonstrate that D-NMF is very useful in separating the waves of an epidemic and identifying a few centers for each wave. © 2022 ACM.

Multi-classification of COVID-19 and pneumonia in chest X-ray images using convolutional neural network

In December 2019, Wuhan became the epicenter of the deadly novel coronavirus (COVID-19), which spread to every corner of the world. Many health systems have seen collapse due to the limited capacity of the system and an exponential increase of suspected COVID-19 cases. A dependable and cost-effective technique is sought to reduce the overloading of radiologists' efforts to detect suspected cases early, which may reduce the patient's death rate. For the detection of COVID-19, this paper has proposed a deep learning multi-classification model. The model is developed using feature extraction by a convolutional neural network (CNN) from chest X-ray images, which could detect COVID-19 and pneumonia correctly. The proposed model is then compared with a pre-trained model, Xception. The proposed model shows 95.53 % accuracy, 0.936 recall, 0.936 precision, 0.936 F1-score while the Xception pre-trained model shows 93.04 % accuracy, 0.936 recall, 0.906 precision, 0.910 F1 score. © 2021 IEEE.

Bidirectional Causality between Spreading COVID-19 and Individual Mobilisation with Consumption Motives across Prefectural Borders in Japan.

A combination of pharmaceutical and non-pharmaceutical interventions as well as social restrictions has been recommended to prevent the spread of coronavirus disease 2019 (COVID-19). Therefore, social contact surveys play an essential role as the basis for more effective measures. This study attempts to explore the fundamental basis of the expansion of COVID-19. Temporal bidirectional causalities between the numbers of newly confirmed COVID-19 cases (NCCC) and individual mobilisations with consumption motives across prefecture borders in three metropolitan regions in Japan were analysed using vector autoregression models. Mobilisation with consumption in pubs from Kanto to Tokai contributed to the spread of COVID-19 in both regions. Meanwhile, causal mobilisation with consumption motives in Kansai also contributed to the expansion of COVID-19; however, the pattern was dependent on the industrial characteristics of each prefecture in Kansai. Furthermore, the number of pub visitors in Kanto immediately decreased when NCCC increased in Kanto. In contrast, the causal mobilisations for the expansion of COVID-19 in the Tokai and Kansai regions were unaffected by the increasing NCCC. These findings partially proved the validity of the conventional governmental measures to suppress pub visitors across prefectural borders. Nevertheless, the individual causal mobilisations with consumption motives that contributed to the increasing COVID-19 cases are not identical nationwide, and thus, regional characteristics should be considered when devising preventive strategies.

A multi-regional, hierarchical-tier mathematical model of the spread and control of COVID-19 epidemics from epicentre to adjacent regions.

Epicentres are the focus of COVID-19 research, whereas emerging regions with mainly imported cases due to population movement are often neglected. Classical compartmental models are useful, however, likely oversimplify the complexity when studying epidemics. This study aimed to develop a multi-regional, hierarchical-tier mathematical model for better understanding the complexity and heterogeneity of COVID-19 spread and control. By incorporating the epidemiological and population flow data, we have successfully constructed a multi-regional, hierarchical-tier SLIHR model. With this model, we revealed insight into how COVID-19 was spread from the epicentre Wuhan to other regions in Mainland China based on the large population flow network data. By comprehensive analysis of the effects of different control measures, we identified that Level 1 emergency response, community prevention and application of big data tools significantly correlate with the effectiveness of local epidemic containment across different provinces of China outside the epicentre. In conclusion, our multi-regional, hierarchical-tier SLIHR model revealed insight into how COVID-19 spread from the epicentre Wuhan to other regions of China, and the subsequent control of local epidemics. These findings bear important implications for many other countries and regions to better understand and respond to their local epidemics associated with the ongoing COVID-19 pandemic.

Change in outbreak epicentre and its impact on the importation risks of COVID-19 progression: A modelling study.

BACKGROUND: The outbreak of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) that was first detected in the city of Wuhan, China has now spread to every inhabitable continent, but now the attention has shifted from China to other epicentres. This study explored early assessment of the influence of spatial proximities and travel patterns from Italy on the further spread of SARS-CoV-2 worldwide. METHODS: Using data on the number of confirmed cases of COVID-19 and air travel data between countries, we applied a stochastic meta-population model to estimate the global spread of COVID-19. Pearson's correlation, semi-variogram, and Moran's Index were used to examine the association and spatial autocorrelation between the number of COVID-19 cases and travel influx (and arrival time) from the source country. RESULTS: We found significant negative association between disease arrival time and number of cases imported from Italy (r = -0.43, p = 0.004) and significant positive association between the number of COVID-19 cases and daily travel influx from Italy (r = 0.39, p = 0.011). Using bivariate Moran's Index analysis, we found evidence of spatial interaction between COVID-19 cases and travel influx (Moran's I = 0.340). Asia-Pacific region is at higher/extreme risk of disease importation from the Chinese epicentre, whereas the rest of Europe, South-America and Africa are more at risk from the Italian epicentre. CONCLUSION: We showed that as the epicentre changes, the dynamics of SARS-CoV-2 spread change to reflect spatial proximities.