Building a system dynamics model to analyze scenarios of COVID-19 policymaking in tourism-dependent developing countries: A case study of Cambodia

To control the COVID-19 pandemic, various policies have been implemented to restrict the mobility of people. Such policies, however, have resulted in huge damages to many economic sectors, especially the tourism sector and its auxiliary services. Focusing on Cambodia, this study presents a system dynamics (SD) model for assessing and selecting effective policy responses to contain the spread of COVID-19, while maintaining tourism development. Policies targeted in this study include international and domestic transportation bans, quarantine policy, tourist-centered protection measures, and enterprise-led protection measures. Two types of scenario analyses are conducted: one targets each policy separately and the other combines different policies. Among all scenarios, quarantine policy is evaluated to be the most effective policy as it balances the containment of the spread of COVID-19 and support for tourism development. This study provides a new way of guiding COVID-19 policymaking and exploring effective policies in the context of tourism.

Spatial and deep learning analyses of urban recovery from the impacts of COVID-19.

This study investigates urban recovery from the COVID-19 pandemic by focusing on three main types of working, commercial, and night-life activities and associating them with land use and inherent socio-economic patterns as well as points of interests (POIs). Massive multi-source and multi-scale data include mobile phone signaling data (500 m × 500 m), aerial images (0.49 m × 0.49 m), night light satellite data (500 m × 500 m), land use data (street-block), and POIs data. Methods of convolutional neural network, guided gradient-weighted class activation mapping, bivariate local indicator of spatial association, Elbow and K-means are jointly applied. It is found that the recovery in central areas was slower than in suburbs, especially in terms of working and night-life activities, showing a donut-shaped spatial pattern. Residential areas with mixed land uses seem more resilient to the pandemic shock. More than 60% of open spaces are highly associated with recovery in areas with high-level pre-pandemic social-economic activities. POIs of sports and recreation are crucial to the recovery in all areas, while POIs of transportation and science/culture are also important to the recovery in many areas. Policy implications are discussed from perspectives of open spaces, public facilities, neighborhood units, spatial structures, and anchoring roles of POIs.

Examining the Human Activity-Intensity Change at Different Stages of the COVID-19 Pandemic across Chinese Working, Residential and Entertainment Areas.

The COVID-19 pandemic has already resulted in more than 6 million deaths worldwide as of December 2022. The COVID-19 has also been greatly affecting the activity of the human population in China and the world. It remains unclear how the human activity-intensity changes have been affected by the COVID-19 spread in China at its different stages along with the lockdown and relaxation policies. We used four days of Location-based services data from Tencent across China to capture the real-time changes in human activity intensity in three stages of COVID-19-namely, during the lockdown, at the first stage of work resuming and at the stage of total work resuming-and observed the changes in different land use categories. We applied the mean decrease Gini (MDG) approach in random forest to examine how these changes are influenced by land attributes, relying on the CART algorithm in Python. This approach was also compared with Geographically Weighted Regression (GWR). Our analysis revealed that the human activity intensity decreased by 22-35%, 9-16% and 6-15%, respectively, in relation to the normal conditions before the spread of COVID-19 during the three periods. The human activity intensity associated with commercial sites, sports facilities/gyms and tourism experienced the relatively largest contraction during the lockdown. During the relaxations of restrictions, government institutions showed a 13.89% rise in intensity at the first stage of work resuming, which was the highest rate among all the working sectors. Furthermore, the GDP and road junction density were more influenced by the change in human activity intensity for all land use categories. The bus stop density was importantly associated with mixed-use land recovery during the relaxing stages, while the coefficient of density of population in entertainment land were relatively higher at these two stages. This study aims to provide additional support to investigate the human activity changes due to the spread of COVID-19 at different stages across different sectors.

The impacts of the built environment factors and population mobility on the spread of COVID-19 during its initial stage of the COVID-19 pandemic: A case of China

The COVID-19 pandemic has greatly disrupted human mobility and economic development globally. The built environment (BE) can contribute to the spread of COVID-19, as well as other infectious diseases, by facilitating human mobility and social contacts between infected and susceptible individuals. It can also provide a space that directly transmits pathogens to the people. Thus more attention should be given to preventing the spread of such diseases through urban planning and management. At present, knowledge about how the built environment affects the COVID-19 spread is limited. In this chapter, we firstly introduce how the built environment can affect public health, then objectively evaluate the influence of key built environment factors on the spread of COVID-19 through a random forest approach across 2994 townships in China in the initial stages of the pandemic. To represent the spread of COVID-19, the ratio of cumulative infection cases (RCIC) and the coefficient of variation of infection cases (CVIC) that reflects the policy effects in the initial stages of the pandemic are selected.

Building a system dynamics model to analyze scenarios of COVID-19 policymaking in tourism-dependent developing countries: A case study of Cambodia

To control the COVID-19 pandemic, various policies have been implemented to restrict the mobility of people. Such policies, however, have resulted in huge damages to many economic sectors, especially the tourism sector and its auxiliary services. Focusing on Cambodia, this study presents a system dynamics (SD) model for assessing and selecting effective policy responses to contain the spread of COVID-19, while maintaining tourism development. Policies targeted in this study include international and domestic transportation bans, quarantine policy, tourist-centered protection measures, and enterprise-led protection measures. Two types of scenario analyses are conducted: one targets each policy separately and the other combines different policies. Among all scenarios, quarantine policy is evaluated to be the most effective policy as it balances the containment of the spread of COVID-19 and support for tourism development. This study provides a new way of guiding COVID-19 policymaking and exploring effective policies in the context of tourism.

"What should be computed" for supporting post-pandemic recovery policymaking? A life-oriented perspective.

The COVID-19 pandemic has caused various impacts on people's lives, while changes in people's lives have shown mixed effects on mitigating the spread of the SARS-CoV-2 virus. Understanding how to capture such two-way interactions is crucial, not only to control the pandemic but also to support post-pandemic urban recovery policies. As suggested by the life-oriented approach, the above interactions exist with respect to a variety of life domains, which form a complex behavior system. Through a review of the literature, this paper first points out inconsistent evidence about behavioral factors affecting the spread of COVID-19, and then argues that existing studies on the impacts of COVID-19 on people's lives have ignored behavioral co-changes in multiple life domains. Furthermore, selected uncertain trends of people's lives for the post-pandemic recovery are described. Finally, this paper concludes with a summary about "what should be computed?" in Computational Urban Science with respect to how to catch up with delays in the SDGs caused by the COVID-19 pandemic, how to address digital divides and dilemmas of e-society, how to capture behavioral co-changes during the post-pandemic recovery process, and how to better manage post-pandemic recovery policymaking processes.

Diverse and nonlinear influences of built environment factors on COVID-19 spread across townships in China at its initial stage.

The built environment can contribute to the spread of the novel coronavirus disease (COVID-19) by facilitating human mobility and social contacts between infected and uninfected individuals. However, mobility data capturing detailed interpersonal transmission at a large scale are not available. In this study, we aimed to objectively assess the influence of key built environment factors, which create spaces for activities-"inferred activity" rather than "actually observed activity"-on the spread of COVID-19 across townships in China at its initial stage through a random forest approach. Taking data for 2994 township-level administrative units, the spread is measured by two indicators: the ratio of cumulative infection cases (RCIC), and the coefficient of variation of infection cases (CVIC) that reflects the policy effect in the initial stage of the spread. Accordingly, we selected 19 explanatory variables covering built environment factors (urban facilities, land use, and transportation infrastructure), the level of nighttime activities, and the inter-city population flow (from Hubei Province). We investigated the spatial agglomerations based on an analysis of bivariate local indicators of spatial association between RCIC and CVIC. We found spatial agglomeration (or positive spatial autocorrelations) of RCIC and CVIC in about 20% of all townships under study. The density of convenience shops, supermarkets and shopping malls (DoCSS), and the inter-city population flow (from Hubei Province) are the two most important variables to explain RCIC, while the population flow is the most important factor in measuring policy effects (CVIC). When the DoCSS gets to 21/km2, the density of comprehensive hospitals to 0.7/km2, the density of road intersections to 72/km2, and the density of gyms and sports centers to 2/km2, their impacts on RCIC reach their maximum and remain constant with further increases in the density values. Stricter policy measures should be taken at townships with a density of colleges and universities higher than 0.5/km2 or a density of comprehensive hospitals higher than 0.25/km2 in order to effectively control the spread of COVID-19.

Effects of transport-related COVID-19 policy measures: A case study of six developed countries.

This study attempts to provide scientifically-sound evidence for designing more effective COVID-19 policies in the transport and public health sectors by comparing 418 policy measures (244 are transport measures) taken in different months of 2020 in Australia, Canada, Japan, New Zealand, the UK, and the US. The effectiveness of each policy is measured using nine indicators of infections and mobilities corresponding to three periods (i.e., one week, two weeks, and one month) before and after policy implementation. All policy measures are categorized based on the PASS approach (P: prepare-protect-provide; A: avoid-adjust; S: shift-share; S: substitute-stop). First, policy effectiveness is compared between policies, between countries, and over time. Second, a dynamic Bayesian multilevel generalized structural equation model is developed to represent dynamic cause-effect relationships between policymaking, its influencing factors and its consequences, within a unified research framework. Third, major policy measures in the six countries are compared. Finally, findings for policymakers are summarized and extensively discussed.

Association of built environment attributes with the spread of COVID-19 at its initial stage in China.

Evidence of the association of built environment (BE) attributes with the spread of COVID-19 remains limited. As an additional effort, this study regresses a ratio of accumulative confirmed infection cases at the city level in China on both inter-city and intra-city BE attributes. A mixed geographically weighted regression model was estimated to accommodate both local and global effects of BE attributes. It is found that spatial clusters are mostly related to low infections in 28.63 % of the cities. The density of point of interests around railway stations, travel time by public transport to activity centers, and the number of flights from Hubei Province are associated with the spread. On average, the most influential BE attribute is the number of trains from Hubei Province. Higher infection ratios are associated with higher values of between-ness centrality in 70.98 % of the cities. In 79.22 % of the cities, the percentage of the aging population shows a negative association. A positive association of the population density in built-up areas is found in 68.75 % of county-level cities. It is concluded that the countermeasures in China could have well reflected spatial heterogeneities, and the BE could be further improved to mitigate the impacts of future pandemics.