Global COVID-19 pandemic demands joint interventions for the suppression of future waves.

Emerging evidence suggests a resurgence of COVID-19 in the coming years. It is thus critical to optimize emergency response planning from a broad, integrated perspective. We developed a mathematical model incorporating climate-driven variation in community transmissions and movement-modulated spatial diffusions of COVID-19 into various intervention scenarios. We find that an intensive 8-wk intervention targeting the reduction of local transmissibility and international travel is efficient and effective. Practically, we suggest a tiered implementation of this strategy where interventions are first implemented at locations in what we call the Global Intervention Hub, followed by timely interventions in secondary high-risk locations. We argue that thinking globally, categorizing locations in a hub-and-spoke intervention network, and acting locally, applying interventions at high-risk areas, is a functional strategy to avert the tremendous burden that would otherwise be placed on public health and society.

A global portrait of expressed mental health signals towards COVID-19 in social media space.

Globally, the COVID-19 pandemic has induced a mental health crisis. Social media data offer a unique opportunity to track the mental health signals of a given population and quantify their negativity towards COVID-19. To date, however, we know little about how negative sentiments differ across countries and how these relate to the shifting policy landscape experienced through the pandemic. Using 2.1 billion individual-level geotagged tweets posted between 1 February 2020 and 31 March 2021, we track, monitor and map the shifts in negativity across 217 countries and unpack its relationship with COVID-19 policies. Findings reveal that there are important geographic, demographic, and socioeconomic disparities of negativity across continents, different levels of a nation's income, population density, and the level of COVID-19 infection. Countries with more stringent policies were associated with lower levels of negativity, a relationship that weakened in later phases of the pandemic. This study provides the first global and multilingual evaluation of the public's real-time mental health signals to COVID-19 at a large spatial and temporal scale. We offer an empirical framework to monitor mental health signals globally, helping international authorizations, including the United Nations and World Health Organization, to design smart country-specific mental health initiatives in response to the ongoing pandemic and future public emergencies.

Measurement of contagion spatial spread probability in public places: A case study on COVID-19.

The scale and scope of the COVID-19 epidemic have highlighted the need for timely control of viral transmission. This paper proposed a new spatial probability model of epidemic infection using an improved Wasserstein distance algorithm and Monte Carlo simulation. This method identifies the public places in which COVID-19 spreads and grows easily. The Wasserstein Distance algorithm is used to calculate the distribution similarity between COVID-19 cases and the public places. Further, we used hypothesis tests and Monte Carlo simulation to estimate the spatial spread probability of COVID-19 in different public places. We used Snow's data to test the stability and accuracy of this measurement. This verification proved that our method is reliable and robust. We applied our method to the detailed geographic data of COVID-19 cases and public places in Wuhan. We found that, rather than financial service institutions and markets, public buildings such as restaurants and hospitals in Wuhan are 95 percent more likely to be the public places of COVID-19 spread.

Estimating Unreported COVID-19 Cases with a Time-Varying SIR Regression Model.

BACKGROUND: Potential unreported infection might impair and mislead policymaking for COVID-19, and the contemporary spread of COVID-19 varies in different counties of the United States. It is necessary to estimate the cases that might be underestimated based on county-level data, to take better countermeasures against COVID-19. We suggested taking time-varying Susceptible-Infected-Recovered (SIR) models with unreported infection rates (UIR) to estimate factual COVID-19 cases in the United States. METHODS: Both the SIR model integrated with unreported infection rates (SIRu) of fixed-time effect and SIRu with time-varying parameters (tvSIRu) were applied to estimate and compare the values of transmission rate (TR), UIR, and infection fatality rate (IFR) based on US county-level COVID-19 data. RESULTS: Based on the US county-level COVID-19 data from 22 January (T1) to 20 August (T212) in 2020, SIRu was first tested and verified by Ordinary Least Squares (OLS) regression. Further regression of SIRu at the county-level showed that the average values of TR, UIR, and IFR were 0.034%, 19.5%, and 0.51% respectively. The ranges of TR, UIR, and IFR for all states ranged from 0.007-0.157 (mean = 0.048), 7.31-185.6 (mean = 38.89), and 0.04-2.22% (mean = 0.22%). Among the time-varying TR equations, the power function showed better fitness, which indicated a decline in TR decreasing from 227.58 (T1) to 0.022 (T212). The general equation of tvSIRu showed that both the UIR and IFR were gradually increasing, wherein, the estimated value of UIR was 9.1 (95%CI 5.7-14.0) and IFR was 0.70% (95%CI 0.52-0.95%) at T212. INTERPRETATION: Despite the declining trend in TR and IFR, the UIR of COVID-19 in the United States is still on the rise, which, it was assumed would decrease with sufficient tests or improved countersues. The US medical system might be largely affected by severe cases amidst a rapid spread of COVID-19.

Racial Segregation, Testing Site Access, and COVID-19 Incidence Rate in Massachusetts, USA.

The U.S. has merely 4% of the world population, but contains 25% of the world's COVID-19 cases. Since the COVID-19 outbreak in the U.S., Massachusetts has been leading other states in the total number of COVID-19 cases. Racial residential segregation is a fundamental cause of racial disparities in health. Moreover, disparities of access to health care have a large impact on COVID-19 cases. Thus, this study estimates racial segregation and disparities in testing site access and employs economic, demographic, and transportation variables at the city/town level in Massachusetts. Spatial regression models are applied to evaluate the relationships between COVID-19 incidence rate and related variables. This is the first study to apply spatial analysis methods across neighborhoods in the U.S. to examine the COVID-19 incidence rate. The findings are: (1) Residential segregations of Hispanic and Non-Hispanic Black/African Americans have a significantly positive association with COVID-19 incidence rate, indicating the higher susceptibility of COVID-19 infections among minority groups. (2) Non-Hispanic Black/African Americans have the shortest drive time to testing sites, followed by Hispanic, Non-Hispanic Asians, and Non-Hispanic Whites. The drive time to testing sites is significantly negatively associated with the COVID-19 incidence rate, implying the importance of the accessibility of testing sites by all populations. (3) Poverty rate and road density are significant explanatory variables. Importantly, overcrowding represented by more than one person per room is a significant variable found to be positively associated with COVID-19 incidence rate, suggesting the effectiveness of social distancing for reducing infection. (4) Different from the findings of previous studies, the elderly population rate is not statistically significantly correlated with the incidence rate because the elderly population in Massachusetts is less distributed in the hotspot regions of COVID-19 infections. The findings in this study provide useful insights for policymakers to propose new strategies to contain the COVID-19 transmissions in Massachusetts.

Building an Open Resources Repository for COVID-19 Research.

The COVID-19 outbreak is a global pandemic declared by the World Health Organization, with rapidly increasing cases in most countries. A wide range of research is urgently needed for understanding the COVID-19 pandemic, such as transmissibility, geographic spreading, risk factors for infections, and economic impacts. Reliable data archive and sharing are essential to jump-start innovative research to combat COVID-19. This research is a collaborative and innovative effort in building such an archive, including the collection of various data resources relevant to COVID-19 research, such as daily cases, social media, population mobility, health facilities, climate, socioeconomic data, research articles, policy and regulation, and global news. Due to the heterogeneity between data sources, our effort also includes processing and integrating different datasets based on GIS (Geographic Information System) base maps to make them relatable and comparable. To keep the data files permanent, we published all open data to the Harvard Dataverse (https://dataverse.harvard.edu/dataverse/2019ncov), an online data management and sharing platform with a permanent Digital Object Identifier number for each dataset. Finally, preliminary studies are conducted based on the shared COVID-19 datasets and revealed different spatial transmission patterns among mainland China, Italy, and the United States.

Fruit and vegetable access differs by community racial composition and socioeconomic position in Detroit, Michigan.

OBJECTIVE: To compare the availability, selection, quality, and price of fresh fruit and vegetables at food stores in four Detroit-area communities: 1) predominately African-American, low socioeconomic position (SEP); 2) racially heterogeneous, low SEP; 3) predominately African-American, middle SEP; and 4) racially heterogeneous, middle SEP. DESIGN: Cross-sectional observational survey, conducted fall 2002. SETTING: Detroit, Michigan SAMPLE: Overall, 304 food stores located in the four communities were evaluated: chain grocery, large independent grocery, "mom-and-pop" grocery, specialty (meat, fruit and vegetable markets), convenience without gasoline, and liquor stores. MAIN OUTCOME MEASURES: Availability was indicated by whether a store carried fresh fruit or vegetables, selection was based on a count of 80 fruit and vegetables, quality was evaluated according to USDA guidelines for a subset of 20 fruit and vegetables, and price was assessed for 20 fruit and vegetables by using the lowest-cost method. RESULTS: Mean quality of fresh produce was significantly lower in the predominately African-American, low-SEP community than in the racially heterogeneous, middle-SEP community. Differences in the types of stores present only partially explained this quality differential. The predominately African-American, low-SEP community had more than four times more liquor stores and fewer grocery stores per 100,000 residents than the racially heterogeneous, middle-SEP community. Mean overall selection and price of fresh produce at stores did not differ among communities. CONCLUSIONS: Increasing access to high-quality fresh produce in low-income communities of color is a critical first step toward improving health through better dietary practices in this population.

A readapted Malone schistosome transmission index model.

Schistosome transmission index can be directly used to give an overall evaluation and prediction of schistosomiasis transmission through a mathematical model. In the light of the mechanism of schistosomiasis transmission process, an improved Malone schistosome transmission index model was established based on the previous study of survivor rate of the snail under extreme temperatures by Hong Qingbiao. Meteorological data from FAO and Institute of Geographic Sciences and Resources Research, CAS was used to calculate the schistosome transmission index by the new model in the schistosomiasis epidemic region of China, and the result was re-classified into transmission index classes. The new model changed the class of "epidemic area" into that of "non-epidemic area" in the regions of the southern Henan province, the northern Anhui province and the middle Jiangsu province by transmission index threshold of 900. The geographic boundary between the epidemic and the non-epidemic regions was found fitly to coincide with the -2 degrees C isotherm of January in China, whereas the underpinning cause needs to be further explored in the future. Finally discussed was the calculation result of this meteorological model having some difference from actual epidemiology as more factors of relevance could be considered to be incorporated into the model, also discussed were a few questions concerning the model itself and the possible future improvement directions.

Neighborhood racial composition, neighborhood poverty, and the spatial accessibility of supermarkets in metropolitan Detroit.

OBJECTIVES: We evaluated the spatial accessibility of large "chain" supermarkets in relation to neighborhood racial composition and poverty. METHODS: We used a geographic information system to measure Manhattan block distance to the nearest supermarket for 869 neighborhoods (census tracts) in metropolitan Detroit. We constructed moving average spatial regression models to adjust for spatial autocorrelation and to test for the effect of modification of percentage African American and percentage poor on distance to the nearest supermarket. RESULTS: Distance to the nearest supermarket was similar among the least impoverished neighborhoods, regardless of racial composition. Among the most impoverished neighborhoods, however, neighborhoods in which African Americans resided were, on average, 1.1 miles further from the nearest supermarket than were White neighborhoods. CONCLUSIONS: Racial residential segregation disproportionately places African Americans in more-impoverished neighborhoods in Detroit and consequently reduces access to supermarkets. However, supermarkets have opened or remained open close to middle-income neighborhoods that have transitioned from White to African American. Development of economically disadvantaged African American neighborhoods is critical to effectively prevent diet-related diseases among this population.