Worldwide Prediction on COVID-19 Spreading Future Trends using Gaussian Model

COVID-19 testing, case, and death rates and spatial socio-demographics in New York City: An ecological analysis as of June 2020.

The Islamic Republic of Iran reported its first COVID-19 cases by 19th February 2020, since then it has become one of the most affected countries, with more than 73,000 cases and 4,585 deaths to this date. Spatial modeling could be used to approach an understanding of structural and sociodemographic factors that have impacted COVID-19 spread at a province-level in Iran. Therefore, in the present paper, we developed a spatial statistical approach to describe how COVID-19 cases are spatially distributed and to identify significant spatial clusters of cases and how socioeconomic and climatic features of Iranian provinces might predict the number of cases. The analyses are applied to cumulative cases of the disease from February 19th to March 18th. They correspond to obtaining maps associated with quartiles for rates of COVID-19 cases smoothed through a Bayesian technique and relative risks, the calculation of global (Moran's I) and local indicators of spatial autocorrelation (LISA), both univariate and bivariate, to derive significant clustering, and the fit of a multivariate spatial lag model considering a set of variables potentially affecting the presence of the disease. We identified a cluster of provinces with significantly higher rates of COVID-19 cases around Tehran (p-value< 0.05), indicating that the COVID-19 spread within Iran was spatially correlated. Urbanized, highly connected provinces with older population structures and higher average temperatures were the most susceptible to present a higher number of COVID-19 cases (p-value < 0.05). Interestingly, literacy is a factor that is associated with a decrease in the number of cases (p-value < 0.05), which might be directly related to health literacy and compliance with public health measures. These features indicate that social distancing, protecting older adults, and vulnerable populations, as well as promoting health literacy, might be useful to reduce SARS-CoV-2 spread in Iran. One limitation of our analysis is that the most updated information we found concerning socioeconomic and climatic features is not for 2020, or even for a same year, so that the obtained associations should be interpreted with caution. Our approach could be applied to model COVID-19 outbreaks in other countries with similar characteristics or in case of an upturn in COVID-19 within Iran.

BackgroundThe spread of COVID-19 at the local level is significantly impacted by population mobility. The U.S. has had extremely high per capita COVID-19 case and death rates. Efficient nonpharmaceutical interventions to control the spread of COVID-19 depend on our understanding of the determinants of public mobility.ObjectiveThis study used publicly available Google data and machine learning to investigate population mobility across a sample of US counties. Statistical analysis was used to examine the socioeconomic, demographic, and political determinants of mobility and the corresponding patterns of per capita COVID-19 case and death rates.MethodsDaily Google population mobility data for 1085 US counties from March 1 to December 31, 2020, were clustered based on differences in mobility patterns using K-means clustering methods. Social mobility indicators (retail, grocery and pharmacy, workplace, and residence) were compared across clusters. Statistical differences in socioeconomic, demographic, and political variables between clusters were explored to identify determinants of mobility. Clusters were matched with daily per capita COVID-19 cases and deaths.ResultsOur results grouped US counties into 4 Google mobility clusters. Clusters with more population mobility had a higher percentage of the population aged 65 years and over, a greater population share of Whites with less than high school and college education, a larger percentage of the population with less than a college education, a lower percentage of the population using public transit to work, and a smaller share of voters who voted for Clinton during the 2016 presidential election. Furthermore, clusters with greater population mobility experienced a sharp increase in per capita COVID-19 case and death rates from November to December 2020.ConclusionsRepublican-leaning counties that are characterized by certain demographic characteristics had higher increases in social mobility and ultimately experienced a more significant incidence of COVID-19 during the latter part of 2020.

Explaining the U.S. rural disadvantage in COVID-19 case and death rates during the Delta-Omicron surge: The role of politics, vaccinations, population health, and social determinants

Did double lockdown strategy backfire? Cobra effect on containment strategy of COVID-19

BackgroundThe influence of meteorological factors on the transmission and spread of COVID-19 is of interest and has not been investigated.ObjectiveThis study aimed to investigate the associations between meteorological factors and the daily number of new cases of COVID-19 in 9 Asian cities.MethodsPearson correlation and generalized additive modeling (GAM) were performed to assess the relationships between daily new COVID-19 cases and meteorological factors (daily average temperature and relative humidity) with the most updated data currently available.ResultsThe Pearson correlation showed that daily new confirmed cases of COVID-19 were more correlated with the average temperature than with relative humidity. Daily new confirmed cases were negatively correlated with the average temperature in Beijing (r=–0.565, P<.001), Shanghai (r=–0.47, P<.001), and Guangzhou (r=–0.53, P<.001). In Japan, however, a positive correlation was observed (r=0.416, P<.001). In most of the cities (Shanghai, Guangzhou, Hong Kong, Seoul, Tokyo, and Kuala Lumpur), GAM analysis showed the number of daily new confirmed cases to be positively associated with both average temperature and relative humidity, especially using lagged 3D modeling where the positive influence of temperature on daily new confirmed cases was discerned in 5 cities (exceptions: Beijing, Wuhan, Korea, and Malaysia). Moreover, the sensitivity analysis showed, by incorporating the city grade and public health measures into the model, that higher temperatures can increase daily new case numbers (beta=0.073, Z=11.594, P<.001) in the lagged 3-day model.ConclusionsThe findings suggest that increased temperature yield increases in daily new cases of COVID-19. Hence, large-scale public health measures and expanded regional research are still required until a vaccine becomes widely available and herd immunity is established.

There is a need to understand population race and ethnicity disparities in the context of sociodemographic risk factors in the US experience of the COVID-19 pandemic. Determine the association between county-level proportion of non-Hispanic Black (NHB) on county COVID-19 case and death rates and observe how this association was influenced by county sociodemographic and health care infrastructure characteristics. This was an ecologic analysis of US counties as of September 20, 2020, that employed stepwise construction of linear and negative binomial regression models. The primary independent variable was the proportion of NHB population in the county. Covariates included county demographic composition, proportion uninsured, proportion living in crowded households, proportion living in poverty, population density, state testing rate, Primary Care Health Professional Shortage Area status, and hospital beds per 1000 population. Outcomes were exponentiated COVID-19 cases per 100 000 population and COVID-19 deaths per 100 000 population. We produced county-level maps of the measures of interest. In total, 3044 of 3142 US counties were included. Bivariate relationships between the proportion of NHB in a county and county COVID-19 case (Exp β = 1.026; 95% confidence interval [CI], 1.024-1.028; P < .001) and death rates (rate ratio [RR] = 1.032; 95% CI, 1.029-1.035; P < .001) were not attenuated in fully adjusted models. The adjusted association between the proportion of NHB population in a county and county COVID-19 case was Exp β = 1.025 (95% CI, 1.023-1.027; P < .001) and the association with county death rates was RR = 1.034 (95% CI, 1.031-1.038; P < .001). The proportion of NHB people in a county was positively associated with county COVID-19 case and death rates and did not change in models that accounted for other socioecologic and health care infrastructure characteristics that have been hypothesized to account for the disproportionate impact of COVID-19 on racial and ethnic minority populations. Results can inform efforts to mitigate the impact of structural racism of COVID-19.

Crime and violation are the threat to justice and meant to be controlled. Accurate crime prediction and future forecasting trends can assist to enhance metropolitan safety computationally. The limited ability of humans to process complex information from big data hinders the early and accurate prediction and forecasting of crime. The accurate estimation of the crime rate, types and hot spots from past patterns creates many computational challenges and opportunities. Despite considerable research efforts, yet there is a need to have a better predictive algorithm, which direct police patrols toward criminal activities. Previous studies are lacking to achieve crime forecasting and prediction accuracy based on learning models. Therefore, this study applied different machine learning algorithms, namely, the logistic regression, support vector machine (SVM), Naïve Bayes, k-nearest neighbors (KNN), decision tree, multilayer perceptron (MLP), random forest, and eXtreme Gradient Boosting (XGBoost), and time series analysis by long-short term memory (LSTM) and autoregressive integrated moving average (ARIMA) model to better fit the crime data. The performance of LSTM for time series analysis was reasonably adequate in order of magnitude of root mean square error (RMSE) and mean absolute error (MAE), on both data sets. Exploratory data analysis predicts more than 35 crime types and suggests a yearly decline in Chicago crime rate, and a slight increase in Los Angeles crime rate; with fewer crimes occurred in February as compared to other months. The overall crime rate in Chicago will continue to increase moderately in the future, with a probable decline in future years. The Los Angeles crime rate and crimes sharply declined, as suggested by the ARIMA model. Moreover, crime forecasting results were further identified in the main regions for both cities. Overall, these results provide early identification of crime, hot spots with higher crime rate, and future trends with improved predictive accuracy than with other methods and are useful for directing police practice and strategies.

Dear Editor, Governmental organizations1 and the American Academy of Dermatology (AAD) recommended that during coronavirus disease (COVID-19), patients with nonessential medical concerns should be rescheduled or offered telemedicine.2 Although most dermatologic visits are nonurgent, some do require urgent evaluation. We, therefore, examined how dermatological practices across the United States have responded and changed their practices. A study population of dermatologic private practices was selected from the AAD's member list.3 A minimum of three private practices were randomly selected for each state and the District of Columbia using a random number generator. To ensure close temporal association between practice changes and COVID-19 case data, we chose to use website information. The use of websites had the advantage over calling in that it allowed us to capture a large sample in a single day (April 22), allowing a truer comparison of the response to the rate of COVID-19 cases at a specific time point. For comparison, we selected the largest academic institution in each state, defined by the institution with the greatest number of dermatology residents. We chose to compare response to the Centers for Disease Control and Prevention reported COVID-19 cases4 to test whether response was related to local disease burden. Differences in groups were tested with Student t-test and correlations with Pearson correlation. A total of 153 private practices' websites and linked-social media accounts were analyzed. Of which, 86.9% of private practices had placed a COVID-19 announcement regarding a change of practice due to COVID-19. For those with an update, 79.7% of practices were open; 73.6% specified restrictions and/or precautions patients should take; 38.3% of private practices indicated they were seeing only urgent cases or limiting nonessential cases; and 36.8% of private practices indicated they were continuing to see all patients or all patients who did not have recent symptoms, exposure, or travel. Importantly, states with a higher COVID-19 rate, >400 cases per 100 000, had a statistically significant higher proportion of closed practices compared with states with <400 cases per 100 000 (P = .0006) (Figure 1). There was a slight correlation between higher rates of cases (R = .25) and the percent of practices closed in individual states, but it was not statistically significant (P = .077). A total of 40 academic dermatology departments were analyzed for comparison. 100% of academic departments had updated their websites or social media accounts with a COVID-19-related message. 62.5% of academic institutions did not clearly indicate whether clinics were open or closed. Only 22.5% of academic clinics indicated they would limit nonessential cases. This study provides an in-depth analysis of how dermatologists responded to COVID-19 case rate. One criticism could be the use of website data, but this allowed more temporally related data, whereas phone calls would have introduced varying delay between COVID-19 rate data extraction and response data. In addition, most surveys are considered accurate if they capture >65% respondents. We have data on 100% of those selected and found that 86.9% provided an update on their website, suggesting that we captured a representative sample. Although academic sites more consistently posted updates, information provided by private dermatology practices were more specific about service changes. Despite being more financially vulnerable, there was a statistically significant higher rate of closure of private practices compared with academic practices (P < .01). In contrast, only 38% of private practices indicated that they would limit nonessential cases despite guideline recommendations. This latter observation could be seen as an inadequate response by providers. However, 87% of private practices were proactive, providing information for patients publicly regarding changes they would implement. Importantly, practices that closed had a significantly higher local rate of COVID-19 cases, suggesting that practice changes were rationally based on local conditions rather than an overall blanket response. The authors declare no conflict of interest.

A new study, released ahead of print by <i>Health Affairs</i>, analyzed California’s racial/ethnic disparities in COVID-19 exposure risks, testing rates, test positivity, and case rates from March to October 2020, combining data from 15.4 million COVID-19 tests with exposure risk estimates from Census Bureau survey data. The authors found that among the COVID-19 cases with known race/ethnicity, 70.0 percent were among Latino people (compared with 39.1 percent of their population share) and White people were underrepresented (19.1 percent of cases and 37.5 percent of the population).

Prediction models for COVID-19 clinical decision making

Infrastructure and building construction are essential sectors driving economic growth, contributing significantly to job creation, technological advancement, and economic development. This research aims to measure the productivity of labor in installing red brick walls and compare it with standards set by regulations. Utilizing observation and data collection methods, followed by comprehensive data analysis, this study assesses on-site labor productivity and benchmarks it against standards from the Ministry of Public Works and Public Housing (PUPR). Additionally, simple forecasting models, such as linear regression, are employed to predict future productivity trends. The findings reveal higher on-site productivity compared to regulatory benchmarks, potentially due to superior management practices, advanced techniques, and favorable working conditions. The predictive model (y = 0.2828x + 5.5273) indicates a positive productivity trend over time, despite daily fluctuations influenced by various factors. The research underscores the importance of aligning field practices with regulatory standards and provides insights into construction sector policy developments. The results offer valuable data for improving the efficiency and effectiveness of construction projects and inform future policy recommendations to enhance labor productivity in the industry.

Objectives: Disease control is important to limit the social, economic and health effects of COVID-19 and reduce the risk of novel variants emerging. Evidence suggests vaccines are less effective against the Omicron variant, but their impact on disease control is unclear. Methods: We used a longitudinal fixed effects Poisson regression model to assess the impact of vaccination on COVID-19 case rates across 32 countries in Europe from 13th October to 01st January 2022. We controlled for country and time fixed effects and the severity of public health restrictions. Results: Full vaccination coverage increased by 4.2%, leading to a 54% reduction in case rates across Europe (p < 0.001). This protection decreased over time but remained significant at 5 weeks after the detection of Omicron. Mean booster vaccination rates increased from 2.71% to 24.5% but provided no significant additional benefit. For every one-unit increase in the severity of public health restrictions, case rates fell by a further 2% (p = 0.019). Conclusion: Full vaccination significantly limited the spread of COVID-19 and blunted the impact of the Omicron variant, despite becoming less useful over time.

Susceptibility to infectious diseases such as COVID-19 depends on how those diseases spread. Many studies have examined the decrease in COVID-19 spread due to reduction in travel. However, less is known about how much functional geographic regions, which capture natural movements and social interactions, limit the spread of COVID-19. To determine boundaries between functional regions, we apply community-detection algorithms to large networks of mobility and social-media connections to construct geographic regions that reflect natural human movement and relationships at the county level in the coterminous United States. We measure COVID-19 case counts, case rates, and case-rate variations across adjacent counties and examine how often COVID-19 crosses the boundaries of these functional regions. We find that regions that we construct using GPS-trace networks and especially commute networks have the lowest COVID-19 case rates along the boundaries, so these regions may reflect natural partitions in COVID-19 transmission. Conversely, regions that we construct from geolocated Facebook friendships and Twitter connections yield less effective partitions. Our analysis reveals that regions that are derived from movement flows are more appropriate geographic units than states for making policy decisions about opening areas for activity, assessing vulnerability of populations, and allocating resources. Our insights are also relevant for policy decisions and public messaging in future emergency situations.

Malaria remains a significant public health concern in Ethiopia. Understanding the historical backgrounds and future trends of malaria incidence is crucial for customizing local specific intervention strategies. The spatiotemporal patterns and putative modeling of future trends of malaria in the Jimma Zone have not been thoroughly investigated. This study aimed to model the spatiotemporal patterns and prediction of future malaria trends in Jimma Zone, Oromia, Ethiopia, Ethiopia from 2022 to 2026. This study analyzed a decade of historical malaria data (2012–2021) from 15 districts of Jimma Zone. A District-level annual parasite incidence (API) and Standardized incidence ratio (SIR) were calculated, and malaria risk maps were developed. Moran’s I and Monte Carlo statistics were used for assessing spatial clustering of malaria incidence, while time series analysis with the Autoregressive Integrated Moving Average (ARIMA) model was used for analyzing the temporal patterns and predicted for the future trends. A total of 1,718,471 individuals were screened for malaria between 2012 and 2021, with 359,655 (20.93%) confirmed cases. Plasmodium falciparum, P. vivax and mixed infections accounted for 52.71%, 43.62% and 3.67%, respectively. Malaria incidence decreased dramatically by 96.13%, with the API falling from 87.97 per 1,000 population at risk in 2012 to 3.40 in 2021. It was revealed that there was random distribution of malaria transmission across districts. API and SIR maps identified potential risk areas and cold spots. The disease exhibited seasonal patterns, peaking from August to November (maximum in September) and from March to June. Despite a decline trend, malaria remains a potential threat in areas like Chora Botor and Shebe Sonbo. Forecasting with the ARIMA (0, 1, 3) x (1, 1, 0) model for 2022–2026 suggests stability in malaria incidence, with seasonal fluctuations but no significant trends. Malaria incidence in Jimma Zone was significantly decreased from 2012 to 2021, reaching notably low risk levels by 2021. However, the disease persists in areas such as Chora Botor and Shebe Sonbo, indicating ongoing risk of transmission. The seasonal pattern of malaria underscores the need for targeted interventions during peak periods. Spatial autocorrelation analysis showed no distinct patterns, suggesting the necessity for continued surveillance in historically high-risk areas. While forecasts suggest a stable malaria cases, maintaining vigilance and adapting interventions based on emerging trends is essential to sustain control progress towards elimination. Finally, future studies should incorporate key factors like climate variables (temperature, rainfall, humidity), topographic features, intervention coverage, and other environmental determinants, which were not included in our analysis.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-32808-y.