Spatio-temporal characteristics and control strategies in the early period of COVID-19 spread: a case study of the mainland China

Spatial association between COVID-19 incidence rate and nighttime light index

This study statistically identified the localised association between socioeconomic conditions and the coronavirus disease 2019 (COVID-19) incidence rate in Thailand on the basis of the 1,727,336 confirmed cases reported nationwide during the first major wave of the pandemic (March-May 2020) and the second one (July 2021-September 2021). The nighttime light (NTL) index, formulated using satellite imagery, was used as a provincial proxy of monthly socioeconomic conditions. Local indicators of spatial association statistics were applied to identify the localised bivariate association between COVID-19 incidence rate and the year-on-year change of NTL index. A statistically significant negative association was observed between the COVID-19 incidence rate and the NTL index in some central and southern provinces in both major pandemic waves. Regression analyses were also conducted using the spatial lag model (SLM) and the spatial error model (SEM). The obtained slope coefficient, for both major waves of the pandemic, revealed a statistically significant negative association between the year-on-year change of NTL index and COVID-19 incidence rate (SLM: coefficient= âˆ'0.0078 and âˆ'0.0064 with P<0.001 and 0.056, respectively; and SEM: coefficient= âˆ'0.0086 and âˆ'0.0083 with P=0.067 and 0.056, respectively). All of the obtained results confirmed the negative association between the COVID-19 pandemic and socioeconomic activity revealing the future extensive applications of satellite imagery as an alternative data source for the timely monitoring of the multidimensional impacts of the pandemic.

Estimating the Economic Effects of the Early Covid-19 Emergency Response in Cities Using Intracity Travel Intensity Data

In the early days of the Covid-19 pandemic, China implemented the most stringent and serious emergency response. To understand the effect of such an emergency response strategy on the economic system, this study proposed a simultaneous overall estimation method using intracity travel intensity data. The overall effect is represented by the difference between intracity travel intensity with and without the emergency response. Using historical data and time series analysis, we compared intracity travel intensity post China’s implementation of the emergency response with predicted intracity travel intensity without such a response. The loss rates, defined by the proportion of intracity travel intensity loss, were calculated for 360 cities within 33 provincial-level regions in China based on data availability. We found that 30 days after the emergency response, 21% of the cities saw over 80% recovery and 10% of the cities showed more than 90% recovery; 45 days after the emergency response, more than 83% of the 360 cities witnessed 80% recovery. The correlation between gross domestic production loss rate and travel intensity loss rate was studied quantitatively to demonstrate the representativeness of the intracity travel intensity loss rate. This indicator was also used to analyze the spatial and temporal patterns of the effects on the economy. The results of this study can help us understand the economic effects caused by the early Covid-19 emergency response and the method can be a reference for fast and real-time economic loss estimation to support emergency response decision making under pandemic conditions.

Data-Driven Analytics Leveraging Artificial Intelligence in the Era of COVID-19: An Insightful Review of Recent Developments

This paper presents the role of artificial intelligence (AI) and other latest technologies that were employed to fight the recent pandemic (i.e., novel coronavirus disease-2019 (COVID-19)). These technologies assisted the early detection/diagnosis, trends analysis, intervention planning, healthcare burden forecasting, comorbidity analysis, and mitigation and control, to name a few. The key-enablers of these technologies was data that was obtained from heterogeneous sources (i.e., social networks (SN), internet of (medical) things (IoT/IoMT), cellular networks, transport usage, epidemiological investigations, and other digital/sensing platforms). To this end, we provide an insightful overview of the role of data-driven analytics leveraging AI in the era of COVID-19. Specifically, we discuss major services that AI can provide in the context of COVID-19 pandemic based on six grounds, (i) AI role in seven different epidemic containment strategies (a.k.a non-pharmaceutical interventions (NPIs)), (ii) AI role in data life cycle phases employed to control pandemic via digital solutions, (iii) AI role in performing analytics on heterogeneous types of data stemming from the COVID-19 pandemic, (iv) AI role in the healthcare sector in the context of COVID-19 pandemic, (v) general-purpose applications of AI in COVID-19 era, and (vi) AI role in drug design and repurposing (e.g., iteratively aligning protein spikes and applying three/four-fold symmetry to yield a low-resolution candidate template) against COVID-19. Further, we discuss the challenges involved in applying AI to the available data and privacy issues that can arise from personal data transitioning into cyberspace. We also provide a concise overview of other latest technologies that were increasingly applied to limit the spread of the ongoing pandemic. Finally, we discuss the avenues of future research in the respective area. This insightful review aims to highlight existing AI-based technological developments and future research dynamics in this area.

Prediction and Analysis of Tourist Management Strategy Based on the SEIR Model during the COVID-19 Period

Tourism destinations are now facing a dilemma choice of controlling the epidemic or developing the economy. This paper takes Macao, a typical international tourist city, as an example to study the strategy of tourist source control during the COVID-19 period. According to the published epidemic data of Macao, this study has established an improved SEIR (Susceptible-Exposed-Infected-Recovered) model, formulated six control strategies against the current epidemic, and used the model above to simulate the time required for all confirmed cases to recover and discharge under different strategies. By taking into consideration the gross revenue of Macao's gambling industry from 2017 to 2019, the impact of different strategies on the economy is predicted and three control strategies are found to be feasible. This study shows that an effective way to break through the above dilemma is to design the tourist management strategy by screening the source of passengers and controlling the upper limit of capacity of destination. These findings provide a scientific basis for tourism destinations in formulating public policies. The improved SEIR model is more consistent with the actual conversion rule of patients in the current COVID-19 epidemic, and it can be applied to further public health related research.