The COVID-19 Pandemic and the International Geographical Union

Having broken out in late 2019, COVID-19 has resulted in a once-in-a-century health emergency that has rapidly evolved into a global socio-economic crisis. As of March 2022, more than 450 million people were infected by the SARS-CoV-2 virus, the cause of COVID-19, resulting in more than six million deaths (WHO, Coronavirus disease (COVID-19) situation dashboard, 2022). The medical systems of many countries have been stretched to the verge of collapse and more than half of the global labor force has stood down. Not only has the pandemic doubled the number of people at risk of starvation to 270 million (Nature, 589:329-330, 2021), but it also pushed 100 million people into poverty in 2020, triggering the worst global recession since World War II (Blake and Wadhwa, 2020 year in review: the impact of COVID-19 in 12 charts, 2020), and increasing the risk of exposure to other pandemics related to ecosystem degradation (IPBES, Workshop report on biodiversity and pandemics of the intergovernmental platform on biodiversity and ecosystem services. Retrieved from Bonn, Germany, 2020;Yin et al., Geogr Sustain 2(1):68-73, 2021). The normal functioning of many organizations has also been hampered by the pandemic and disruptions to the global travel and tourism industry have been unprecedented. By way of an example, travel restrictions led to the postponement of the 2020 34th International Geographical Congress to the following year and, ultimately, the decision was made to transition to an entirely online format for the event. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Nowcasting COVID-19 deaths in England by age and region.

Understanding the trajectory of the daily number of COVID-19 deaths is essential to decisions on how to respond to the pandemic, but estimating this trajectory is complicated by the delay between deaths occurring and being reported. In England the delay is typically several days, but it can be weeks. This causes considerable uncertainty about how many deaths occurred in recent days. Here we estimate the deaths per day in five age strata within seven English regions, using a Bayesian model that accounts for reporting-day effects and longer-term changes in the delay distribution. We show how the model can be computationally efficiently fitted when the delay distribution is the same in multiple strata, for example, over a wide range of ages.

A coalitional game-based joint monitoring mechanism for combating COVID-19.

In the absence of effective treatment for COVID-19, disease prevention and control have become a top priority across the world. However, the general lack of effective cooperation between communities makes it difficult to suppress the community spread of the global pandemic; hence repeated outbreaks of COVID-19 have become the norm. To address this problem, this paper considers community cooperation in disease monitoring and designs a joint epidemic monitoring mechanism, in which adjacent communities cooperate to enhance their monitoring capability. In this work, we formulate the epidemiological monitoring process as a coalitional game. Then, we propose a Shapley value-based payoffs distribution scheme for the coalitional game. A comprehensive analytical framework is developed to evaluate the advantages and sustainability of the cooperation between communities. Experimental results show that the proposed mechanism performs much better than the conventional non-cooperative monitoring design and can greatly increase each community's payoffs.

IoT-Based Epidemic Monitoring via Improved Gated Recurrent Unit Model

During the Coronavirus Disease 2019 (COVID-19) pandemic, non-contact health monitoring and human activity detection by various sensors have attracted tremendous attention. Robot monitoring will result in minimizing the life threat to health providers during the COVID-19 pandemic period. How to improve the performance and generalization of the monitoring model is a critical but challenging task. This paper constructs an epidemic monitoring architecture based on multi-sensor information fusion and applies it in medical robots’ services, such as patient-care, disinfection, garbage disposal, etc. We propose a gated recurrent unit model based on a genetic algorithm (GA-GRU)to realize the effective feature selection and improve the effectiveness and accuracy of the localization, navigation, and activity monitoring for indoor wireless sensor networks (WSNs). By using two GRU layers in the GA-GRU, we improve the generalization capability in multiple WSNs. All these advantages of GA-GRU make it outperform other representative algorithms in a variety of evaluation metrics. The experiments on the WSNs verify that the proposed GA-GRU leads to successful runs and provides optimal performances. These results suggest the GA-GRU method may be preferable for epidemic monitoring in medicine and allied areas with particular relation to the control of the epidemic or pandemic such as COVID-19 pandemic.

Epidemic Data Visualization Surveillance Based on Flask

With the spread of the epidemic, the development of digital industry will be rapidly enhanced in the new situation and opportunities. The combination of mathematical model and graph makes it possible to predict the trend of infectious diseases according to the different transmission speed, spatial scope, transmission route diversity, dynamic mechanism and other factors. The visualization technology of infectious disease model data also plays an important role in epidemic data detection. In this paper, real-time monitoring, quantitative analysis, dynamic prediction and assessment of the current severe situation of the COVID-19 epidemic were conducted to obtain relevant information on the development of the epidemic, objectively estimate the current situation of the COVID-19 epidemic, and predict the development trend in the future. The innovative drag-and-drop recalculation, data viewing, distance roaming and other functions in this paper have greatly improved user experience and enabled users to have the ability of data mining and integration. It provides a one-stop solution, which is from the template, ORM, Session and the Authentication background. It is very convenient to use. The monitoring and trend prediction platform has also naturally become a powerful helper for the government to realize comprehensive monitoring and decision-making on COVID-19. © 2022 IEEE.

Application of the Internet Platform in Monitoring Chinese Public Attention to the Outbreak of COVID-19.

Objectives: The internet data is an essential tool for reflecting public attention to hot issues. This study aimed to use the Baidu Index (BDI) and Sina Micro Index (SMI) to confirm correlation between COVID-19 case data and Chinese online data (public attention). This could verify the effect of online data on early warning of public health events, which will enable us to respond in a more timely and effective manner. Methods: Spearman correlation was used to check the consistency of BDI and SMI. Time lag cross-correlation analysis of BDI, SMI and six case-related indicators and multiple linear regression prediction were performed to explore the correlation between public concern and the actual epidemic. Results: The public's usage trend of the Baidu search engine and Sina Weibo was consistent during the COVID-19 outbreak. BDI, SMI and COVID-19 indicators had significant advance or lag effects, among which SMI and six indicators all had advance effects while BDI only had advance effects with new confirmed cases and new death cases. But compared with the SMI, the BDI was more closely related to the epidemic severity. Notably, the prediction model constructed by BDI and SMI can well fit new confirmed cases and new death cases. Conclusions: The confirmed associations between the public's attention to the outbreak of COVID and the trend of epidemic outbreaks implied valuable insights into effective mechanisms of crisis response. In response to public health emergencies, people can through the information recommendation functions of social media and search engines (such as Weibo hot search and Baidu homepage recommendation) to raise awareness of available disease prevention and treatment, health services, and policy change.

Monitoring and Analysis of COVID-19 Pandemic: The Need for an Empirical Approach.

The current worldwide pandemic produced by coronavirus disease 2019 (COVID-19) has changed the paradigm of mathematical epidemiology due to the high number of unknowns of this new disease. Thus, the empirical approach has emerged as a robust tool to analyze the actual situation carried by the countries and also allows us to predict the incoming scenarios. In this paper, we propose three empirical indexes to estimate the state of the pandemic. These indexes quantify both the propagation and the number of estimated cases, allowing us to accurately determine the real risk of a country. We have calculated these indexes' evolution for several European countries. Risk diagrams are introduced as a tool to visualize the evolution of a country and evaluate its current risk as a function of the number of contagious individuals and the empiric reproduction number. Risk diagrams at the regional level are useful to observe heterogeneity on COVID-19 penetration and spreading in some countries, which is essential during deconfinement processes. During the pandemic, there have been significant differences seen in countries reporting case criterion and detection capacity. Therefore, we have introduced estimations about the real number of infectious cases that allows us to have a broader view and to better estimate the risk. These diagrams and indexes have been successfully used for the monitoring of European countries and regions during the COVID-19 pandemic.

Estimation of SARS-CoV-2 Infection Fatality Rate by Real-time Antibody Screening of Blood Donors.

BACKGROUND: The pandemic due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has tremendous consequences for our societies. Knowledge of the seroprevalence of SARS-CoV-2 is needed to accurately monitor the spread of the epidemic and to calculate the infection fatality rate (IFR). These measures may help the authorities make informed decisions and adjust the current societal interventions. The objective was to perform nationwide real-time seroprevalence surveying among blood donors as a tool to estimate previous SARS-CoV-2 infections and the population-based IFR. METHODS: Danish blood donors aged 17-69 years giving blood 6 April to 3 May were tested for SARS-CoV-2 immunoglobulin M and G antibodies using a commercial lateral flow test. Antibody status was compared between geographical areas, and an estimate of the IFR was calculated. Seroprevalence was adjusted for assay sensitivity and specificity taking the uncertainties of the test validation into account when reporting the 95% confidence intervals (CIs). RESULTS: The first 20 640 blood donors were tested, and a combined adjusted seroprevalence of 1.9% (95% CI, .8-2.3) was calculated. The seroprevalence differed across areas. Using available data on fatalities and population numbers, a combined IFR in patients <70 years is estimated at 89 per 100 000 (95% CI, 72-211) infections. CONCLUSIONS: The IFR was estimated to be slightly lower than previously reported from other countries not using seroprevalence data. The IFR is likely severalfold lower than the current estimate. We have initiated real-time nationwide anti-SARS-CoV-2 seroprevalence surveying of blood donations as a tool in monitoring the epidemic.