Spatial Autocorrelation Test on the Average Unemployment in Indonesia

According to World Bank statistics in 2019, Indonesia ranked two in the average unemployment rate with 5.28% in South East Asia. Although the unemployment rate can be reduced by an equitable distribution of human resource empowerment and national development, the global pandemic COVID-19 made a major impact on increasing the rate of unemployment. This paper tests the spatial autocorrelation on the average unemployment in Indonesia using Ordinary Least Squares (OLS) and Moran's I. The OLS method was used to examine the effects that affect the unemployment rate using an independent variable. In contrast, the Moran's I used to prove the existence of spatial effect on the level of movement in Indonesia. From the experiment, there are four variables that influence the unemployment rate by using the OLS modeling method. The Moran's I test showed a p-value = 0.006 with α = 0.05. Therefore, there is a spatial autocorrelation between provinces in Indonesia. In addition, the model is tested using the Variance Inflation Factor. The model showed a VIF score ¡10, therefore there is no collinearity and the assumption is fulfilled. The model is also being tested using dwtest, bptest, and Lilliefors test. The result showed p-value = 0.6231 for dwtest, p-value = 0.932 for bptest, and p-value = 0.08438 for Lilliefors test.. © 2022 IEEE.

Retrospective Spatial Analysis of the COVID-19 Threat to the High-risk Population in Davao Region, Philippines from March 2020 to August 2021

According to the World Health Organization (WHO), the elderly and people with comorbidities are most vulnerable to COVID-19 infection. With this, the challenges and threats posed to the vulnerable population require targeted interventions. While public health surveillance methods had developed recent advances to meet users' information needs, the volume and complexity of infectious disease data had increased, resulting in increasing difficulty to facilitate risk communication with the public and for decision-makers to make informed measures to protect the public's health. Moreover, the implementation of COVID-19 spatiotemporal disease surveillance strategies specifically targeting the vulnerable population in the Davao Region had been previously unexplored. This paper investigated the COVID-19 incidence in the Davao Region from 03 Mar 2020, the earliest recorded date of onset, to 31 Aug 2021 using geospatial tools. The variables were visualized through choropleth maps and graduated symbols, and subsequently examined through spatial autocorrelation and hotspot analysis. Hotspots across the region were observed to be in high-density areas. These areas pose greater risks of infection due to the presence of a high concentration of cases. However, high case fatality rates were found in far-flung municipalities where access to COVID-19 healthcare facilities is a dilemma. In the COVID-19 setting and future disease outbreaks similar to COVID-19, results from this study may provide insights to government offices and other related agencies to improve healthcare systems and programs such as providing and initiating tailor-fitted isolation and consultation mechanisms appropriate to the vulnerable population in a community. [ FROM AUTHOR] Copyright of Philippine Journal of Science is the property of Science & Technology Information Institute and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Clean Energy Action Index Efficiency: An Analysis in Global Uncertainty Contexts

Climate change, the scarcity of fossil fuels, advances in clean energy, and volatility of crude oil prices have led to the recognition of clean energy as a viable alternative to dirty energy. This paper investigates the multifractal scaling behavior and efficiency of green finance markets, as well as traditional markets such as gold, crude oil, and natural gas between 1 January 2018, and 9 March 2023. To test the serial dependency (autocorrelation) and the efficient market hypothesis, in its weak form, we employed the Lo and Mackinlay test and the DFA method. The empirical findings showed that returns data series exhibit signs of (in)efficiency. Additionally, there is a negative autocorrelation among the crude oil market, the Clean Energy Fuels Index, the Global Clean Energy Index, the gold market, and the natural gas market. Arbitration strategies can be used to obtain abnormal returns, but caution should be exercised as prices may increase above their actual market value and reduce the profitability of trading. This work contributes to the body of knowledge on sustainable finance by teaching investors how to use predictive strategies on the future values of their investments.

Effect of geographical factors on reference values of creatine kinase isoenzyme.

The aim of this study was to investigate the geographical spatial distribution of creatine kinase isoenzyme (CK-MB) in order to provide a scientific basis for clinical examination. The reference values of CK-MB of 8697 healthy adults in 137 cities in China were collected by reading a large number of literates. Moran index was used to determine the spatial relationship, and 24 factors were selected, which belonged to terrain, climate, and soil indexes. Correlation analysis was conducted between CK-MB and geographical factors to determine significance, and 9 significance factors were extracted. Based on R language to evaluate the degree of multicollinearity of the model, CK-MB Ridge model, Lasso model, and PCA model were established, through calculating the relative error to choose the best model PCA, testing the normality of the predicted values, and choosing the disjunctive kriging interpolation to make the geographical distribution. The results show that CK-MB reference values of healthy adults were generally correlated with latitude, annual sunshine duration, annual mean relative humidity, annual precipitation amount, and annual range of air temperature and significantly correlated with annual mean air temperature, topsoil gravel content, topsoil cation exchange capacity in clay, and topsoil cation exchange capacity in silt. The geospatial distribution map shows that on the whole, it is higher in the north and lower in the south, and gradually increases from the southeast coastal area to the northwest inland area. If the geographical factors are obtained in a location, the CK-MB model can be used to predict the CK-MB of healthy adults in the region, which provides a reference for us to consider regional differences in clinical diagnosis.

COVID-19 in Toronto: Investigating the Spatial Impact of Retailers in the Food Retail and Food Service Sector

COVID-19 has had a significant impact on the global economy. The retailing sector, which relies heavily on high levels of human interaction, has experienced the worst impact. This study aimed to assess the spatial distribution of COVID-19 in Toronto and its impact on business locations from the food retail and food service sectors by investigating four retailers: Starbucks, McDonald's, Shoppers Drug Mart, and Loblaws. Kernel density estimation revealed that the spatial distribution of COVID-19 incidences in the City of Toronto is uneven, with a high density of cases present in the Downtown core. Spatial autocorrelation was performed at the global and local levels to assess the spatial pattern of Starbucks, McDonald's, Shoppers Drug Mart, and Loblaws locations. The findings revealed that retailers spatially clustered in a COVID-19 hotspot are the most impacted. Further to this analysis, a geographically weighted regression model was generated, which indicated a strong correlation between COVID-19 and low socio-economic status. This allows for a better understanding of the characteristics associated with the retail locations at risk from COVID-19, enabling retailers to make strategic adjustments to respond to a rapidly changing marketplace. © 2023, Springer Nature Switzerland AG.

Spatial and Socioeconomic Analysis of Purposeful Mobile Internet Use in US States

The digital divide in the United States has received renewed attention during the COVID-19 pandemic. As achievement of digital equity remains a high priority, this study examines spatial patterns and socioeconomic determinants of the purposeful use of mobile internet for personal and business needs in US states. Agglomerations of mobile internet use are identified using K-means clustering and the extent of agglomeration is measured using spatial autocorrelation analysis. Regression analysis reveals that mobile internet use is associated with employment in management, business, science, and arts occupations, affordability, age structure, and the extent of freedom in US states. Spatial randomness of regression residuals shows the effectiveness of the conceptual model to account for spatial bias. Implications of these findings are discussed. © 2023 IEEE Computer Society. All rights reserved.

VASA: an exploratory visualization tool for mapping spatio-temporal structure of mobility–a COVID-19 case study

Exploratory data analysis tools designed to measure global and local spatial autocorrelation (e.g. Moran's (Formula presented.) statistic) have become standard in modern GIS software. However, there has been little development in amending these tools for visualization and analysis of patterns captured in spatio-temporal data. We design and implement an exploratory mapping tool, VASA (Visual Analysis for Spatial Association), that streamlines analytical pipelines in assessing spatio-temporal structure of data and enables enhanced visual display of the patterns captured in data. Specifically, VASA applies a set of cartographic visual variables to map local measures of spatial autocorrelation and helps delineate micro and macro trends in space-time processes. Two visual displays are presented: recency and consistency map and line-scatter plots. The former combines spatial and temporal data view of local clusters, while the latter drills down on the temporal trends of the phenomena. As a case study, we demonstrate the usability of VASA for the investigation of mobility patterns in response to the COVID-19 pandemic throughout 2020 in the United States. Using daily county-level and grid-level mobility metrics obtained from three different sources (SafeGraph, Cuebiq, and Mapbox), we demonstrate cartographic functionality of VASA for a swift exploratory analysis and comparison of mobility trends at different regional scales. © 2023 Cartography and Geographic Information Society.

The study of spatial autocorrelation for infectious disease epidemiology decision-making: a systematized literature review

In recent years, the global spread of communicable diseases such as Ebola and COVID-19 has stressed the need for clear, geographically targeted, and actionable public health recommendations at appropriate spatial scales. Country-level stakeholders are increasingly utilizing spatial data and spatial decision support systems to optimize resource allocation, and researchers have access to a growing library of spatial data, tools, and software. Application of spatial methods, however, varies widely between researchers, resulting in often unstandardized results, which may be difficult to compare across geographical settings. This literature review aims to compare epidemiological studies, which applies methods including spatial autocorrelation to describe, explain, or predict spatial patterns underlying infectious disease health outcomes, and to describe whether those studies provide clear recommendations.The results of our analysis show an increasing trend in the number of publications applying spatial analysis in epidemiological research per year, with COVID-19, tuberculosis and dengue predominantly studied (43% of n = 98 total articles), and a majority of publication coming from Asia (62%). Spatial autocorrelation was quantified in the majority of studies (72%), and 57 (58%) of articles include some form of statistical modeling of which 11 (19%) accounted for spatial autocorrelation in the model. Most studies (68%) provided some level of recommendation regarding how results should be interpreted for future research or policy development, however often using vague, cautious terms. We recommend the development of standards for spatial epidemiological methods and reporting, and for spatial epidemiological studies to more clearly propose how their findings support or challenge current public health practice. © CAB International 2022 (Online ISSN 1749-8848)

Integration of Moran's I, geographically weighted regression (GWR), and ordinary least square (OLS) models in spatiotemporal modeling of COVID-19 outbreak in Qom and Mazandaran Provinces, Iran.

Globally, the COVID-19 pandemic is a top-level public health concern. This paper attempts to identify the COVID-19 pandemic in Qom and Mazandaran provinces, Iran using spatial analysis approaches. This study was based on secondary data of confirmed cases and deaths from February 3, 2020, to late October 2021, in two Qom and Mazandaran provinces from hospitals and the website of the National Institute of Health. In this paper, three geographical models in ArcGIS 10.8.1 were utilized to analyze and evaluate COVID-19, including geographic weight regression (GWR), ordinary least squares (OLS), and spatial autocorrelation (Moran I). The results from this study indicate that the rate of scattering of confirmed cases for Qom province for the period was 44.25%, while the rate of dispersal of the deaths was 4.34%. Based on the GWR and OLS model, Moran's statistics demonstrated that confirmed cases, deaths, and recovered followed a clustering pattern during the study period. Moran's Z-score for all three indicators of confirmed cases, deaths, and recovered was confirmed to be greater than 2.5 (95% confidence level) for both GWR and OLS models. The spatial distribution of indicators of confirmed cases, deaths, and recovered based on the GWR model has been more scattered in the northwestern and southwestern cities of Qom province. Whereas the spatial distribution of the recoveries of the COVID-19 pandemic in Qom province was 61.7%, the central regions of this province had the highest spread of recoveries. The spatial spread of the COVID-19 pandemic from February 3, 2020, to October 2021 in Mazandaran province was 35.57%, of which 2.61% died, according to information published by the COVID-19 pandemic headquarters. Most confirmed cases and deaths are scattered in the north of this province. The ordinary least squares model results showed that the spatial dispersion of recovered people from the COVID-19 pandemic is more significant in the central and southern regions of Mazandaran province. The Z-score for the deaths Index is more significant than 14.314. The results obtained from this study and the information published by the National Headquarters for the fight against the COVID-19 pandemic showed that tourism and pilgrimages are possible factors for the spatial distribution of the COVID-19 pandemic in Qom and Mazandaran provinces. The spatial information obtained from these modeling approaches could provide general insights to authorities and researchers for further targeted investigations and policies in similar circumcises.

The propagation of European airports' on-time performance and on-time flights via air connectivity prior to the Covid-19 pandemic.

This research investigates the number of on-time flights (OTFs) at European airports and how this number is influenced by an airport's flight connectivity. We conduct a spatial statistical analysis of the spatial context relationship using econometric models, and the interaction between the number of airport's on-time flights (OTFs) and flight connectivity. Using 2017 and 2018 data, we characterize the relationship between a European airport's air connectivity index (ACI) and the number of flights that depart or arrive at a gate within 15 min of schedule (OTFs). We also analyze the relationship between OTFs at a given airport and those of neighboring airports. As the distances between airports increase, autocorrelation shifts from a positive to a negative sign meaning that at greater distances, airports' on-time performance is less dissimilar. We find that before the pandemic and the ensuing global travel shutdown, a spatially lagged term of ACI improves the model's ability to account for variations in OTFs across airports. Flight delay propagation in the air transport system caused delays to occur due to the shared resources underlying an initially delayed flight and subsequent flights. This analysis offers a rational for increasing airport connectivity as a way of improving the share of on-time flights of European airports.

Spatial-temporal variations and influencing factors of air quality in China's major cities during COVID-19 lockdown.

To control the spread of COVID-19, the Chinese government announced a "lockdown" policy, and the citizens' activities were restricted. This study selected three standard air quality indexes, AQI, PM2.5, and PM10, of 2017-2021 in 40 major cities in six regions in China to analyze their changes, spatial-temporal distributions, and socioeconomic influencing factors. Compared with 2019, the values of AQI, PM2.5, and PM10 decreased, and the days with AQI levels "AQI ≤ 100" increased during the "lockdown" in 2020. Due to different degrees of industrialization, the concentration of air pollutants shows significant regional characteristics. The AQI values before and after the "lockdown" in 2020 show significant spatial autocorrelation, and the cities' AQI values in the north present high autocorrelation, and the cities in the south are in low autocorrelation. From the data at the national level, carbon emission intensity (CEI), per capita energy consumption (PEC), per capita GDP (PCG), industrialization rate (IR), and proportion of construction value added (PCVA) have the greatest impact on AQI. This study gives regulators confidence that if the government implements regionalized air quality improvement policies according to the characteristics of each region in China and reasonably plans socioeconomic activities, it is expected to improve China's air quality sustainably.

Bayesian Hierarchical Spatial Modeling of COVID-19 Cases in Bangladesh

This research aimed to investigate the spatial autocorrelation and heterogeneity throughout Bangladesh's 64 districts. Moran I and Geary C are used to measure spatial autocorrelation. Different conventional models, such as Poisson-Gamma and Poisson-Lognormal, and spatial models, such as Conditional Autoregressive (CAR) Model, Convolution Model, and modified CAR Model, have been employed to detect the spatial heterogeneity. Bayesian hierarchical methods via Gibbs sampling are used to implement these models. The best model is selected using the Deviance Information Criterion. Results revealed Dhaka has the highest relative risk due to the city's high population density and growth rate. This study identifies which district has the highest relative risk and which districts adjacent to that district also have a high risk, which allows for the appropriate actions to be taken by the government agencies and communities to mitigate the risk effect. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

Persistence On Tourism In North Macedonia: The Impact Of COVID-19

This study analyzes the level of tourism persistence in the North Macedonia through predictors of foreign arrivals and overnight stays for the time period of annual data from 1956 to 2020 and for monthly data from January 2010 to October 2021 by applying fractional integration techniques. The results show that for the annual data shocks are temporary by applying autocorrelation model. However, at the monthly data the shocks are expected to have permanent effects. The government should react further in trying to bring back the tourism figures as before the pandemic COVID-19. Copyright © 2022 The Authors.

Observed versus estimated actual trend of COVID-19 case numbers in Cameroon: A data-driven modelling.

Controlling the COVID-19 outbreak remains a challenge for Cameroon, as it is for many other countries worldwide. The number of confirmed cases reported by health authorities in Cameroon is based on observational data, which is not nationally representative. The actual extent of the outbreak from the time when the first case was reported in the country to now remains unclear. This study aimed to estimate and model the actual trend in the number of COVID -19 new infections in Cameroon from March 05, 2020 to May 31, 2021 based on an observed disaggregated dataset. We used a large disaggregated dataset, and multilevel regression and poststratification model was applied prospectively for COVID-19 cases trend estimation in Cameroon from March 05, 2020 to May 31, 2021. Subsequently, seasonal autoregressive integrated moving average (SARIMA) modeling was used for forecasting purposes. Based on the prospective MRP modeling findings, a total of about 7450935 (30%) of COVID-19 cases was estimated from March 05, 2020 to May 31, 2021 in Cameroon. Generally, the reported number of COVID-19 infection cases in Cameroon during this period underestimated the estimated actual number by about 94 times. The forecasting indicated a succession of two waves of the outbreak in the next two years following May 31, 2021. If no action is taken, there could be many waves of the outbreak in the future. To avoid such situations which could be a threat to global health, public health authorities should effectively monitor compliance with preventive measures in the population and implement strategies to increase vaccination coverage in the population.

Information Visualization Technology and Mathematical Statistics Applied in the Research on Spatiotemporal Clustering and Changing of COVID-19

To analyze the epidemiological and distribution characteristics of COVID-19 in the United States from 2020.1 to 2021.8, which can provide scientific basis for the formulation of epidemic prevention measures. The incidence data of COVID-19 epidemic from 2020.1-2021.8 in the United States were collected for analysis, the spatial autocorrelation was analyzed by using Geoda 1.18.0, SaTScan 10.0 was used to conduct spatial scan statistics, and ArcGIS 10.4 were used to visualize. As of August 26, 2021, the epidemic in the United States is still in a state of high-speed transmission, and the number of cases is concentrated from November 2020 to February 2021 and August 2021;From the perspective of global spatial autocorrelation, COVID-19 in the United States has a high spatial aggregation, and the geographical spatial adjacency of each region has the greatest influence on the intensity of disease aggregation. According to the local spatial self-analysis, most of the agglomerations were in high-high and low-low clusters, and the high-high cluster states showed a patchy distribution, and experienced an increase-decrease-increase in number. According to the spatio-temporal scanning statistics, there were four clusters, of which the first cluster was located in the southeastern United States. In terms of t the mean center of infection, the epidemic moved greatly in the early stage and stabilized in the southeastern part of the United States in the later stage. COVID-19 in the United States has strong aggregation and changes over time. The focus of prevention and control is the southeast of the United States, and the focus of prevention and control is to reduce the population movement of adjacent states. © 2022 IEEE.

Burden of COVID-19 Risk in the Municipalities of East Java Province, Indonesia, and its Associated Factors: Spatial Analysis Approach

East Java Province has the fourth-highest number of COVID-19 cases among all other provinces Indonesia. This study aimed to examine the spatial effect on confirmed cases of COVID-19 and the risk factors. Data were analyzed using Geoda software to obtain Global Moran's Index and Local Spatial Autocorrelation (LISA) and QGIS 2.8.1 software to make a map. Moran's I scatter plots also used to exploring the bivariate association between COVID-19 cases and potential predictors. The Global Moran's I statistics value shows spatial clustering in COVID-19 cases across the municipalities of East Java Province (Moran's I=0.3986). A positive spatial autocorrelation was observed between COVID-19 cases and population density (Moran's I = 0.2059), vaccination coverage (Moran's I = 0.322), the number of laboratories (Moran's I = 0.2322), ratio of health worker (Moran's I = 0.1617), and household (Moran's I = 0.0866). In comparison, a negative spatial correlation was observed between COVID-19 cases and The Enforcement of Restrictions on Community Activities' levels (Moran's I =-0,2420), average number of family member (Moran's I = 0.0115). The LISA cluster map shows that there were 3 hot spots (Surabaya, Gresik, and Sidoarjo) and 3 cold spots (Sampang, Pamekasan, and Sumenep). Copyright © 2023, Institute of Medico-legal Publication. All rights reserved.

Spatio-Temporal Variations of the COVID-19 Epidemic in Mexico

Mexico is one of the countries worst affected by the Coronavirus Disease 2019 (COVID-19). Analyzing the spatiotemporal spread processes of the COVID-19 epidemic in Mexico is of great significance in terms of preventing its further transmission. This study obtained COVID-19 cases and deaths at the municipality level in Mexico from February 28, 2020, to February 27, 2022, and adopted Hoover index, spatial autocorrelation analysis, and epidemic center calculation to reveal the spatio-temporal pattern of the pandemic nationwide. The results showed that the COVID-19 outbreak in Mexico experienced an initial low-level transmission and four concentrated outbreaks. In terms of spatial transmission pattern, COVID-19 cases showed clear spatial clustering characteristics (Moran's I: 0.48), and large cities with more social interactions (such as Mexico City, Guadalajara, etc.) were most affected. In terms of the directional characteristics of the COVID-19 impact, the epidemiological center constantly shifted in the northeast-southwest direction due to the changing severity of the epidemic in the northwestern coast and the central part of Mexico during the initial outbreak phase. Accordingly, the centers of the three subsequent outbreaks moved to the southeast, northwest, and southeast. The COVID-19 epidemic spread very rapidly in Mexico, especially in the second phase. In the four concentrated outbreaks, the time for the distribution of cases to form a relatively stable spatial pattern was 99 days, 15 days, 95 days, and 42 days, respectively. But the difference of transmission rate at the state level is significant. The state with earlier outbreaks, such as Mexico City, spreads faster. This study revealed the characteristics and laws of the spread of infectious diseases at the national scale, and provided a reference for the prevention and control of the COVID-19 epidemic and future emerging infectious diseases. © 2022 IEEE.

Surveillance of SARS-CoV-2 variants of concern (VOC) in hospital wastewater (WW) and its correlation with hospitalized cases of COVID-19 and the occurrence of outbreaks

Background. WW surveillance enables real time monitoring of SARS-CoV-2 burden in defined sewer catchment areas. Here, we assessed the occurrence of total, Delta and Omicron SARS-CoV-2 RNA in sewage from three tertiary-care hospitals in Calgary, Canada. Methods. Nucleic acid was extracted from hospital (H) WW using the 4S-silica column method. H-1 and H-2 were assessed via a single autosampler whereas H-3 required three separate monitoring devices (a-c). SARS-CoV-2 RNA was quantified using two RT-qPCR approaches targeting the nucleocapsid gene;N1 and N200 assays, and the R203K/G204R and R203M mutations. Assays were positive if Cq< 40. Cross-correlation function analyses (CCF) was performed to determine the timelagged relationships betweenWWsignal and clinical cases. SARS-CoV-2 RNA abundance was compared to total hospitalized cases, nosocomial-acquired cases, and outbreaks. Statistical analyses were conducted using R. Results. Ninety-six percent (188/196) of WW samples collected between Aug/ 21-Jan/22 were positive for SARS-CoV-2. Omicron rapidly supplanted Delta by mid-December and this correlated with lack of Delta-associated H-transmissions during a period of frequent outbreaks. The CCF analysis showed a positive autocorrelation between the RNA concentration and total cases, where the most dominant cross correlations occurred between -3 and 0 lags (weeks) (Cross-correlation values: 0.75, 0.579, 0.608, 0.528 and 0.746 for H-1, H-2, H-3a, H-3b and H-3c;respectively). VOC-specific assessments showed this positive association only to hold true for Omicron across all hospitals (cross-correlation occurred at lags -2 and 0, CFF value range between 0.648 -0.984). We observed a significant difference in median copies/ ml SARS-CoV-2 N-1 between outbreak-free periods vs outbreaks for H-1 (46 [IQR: 11-150] vs 742 [IQR: 162-1176], P< 0.0001), H-2 (24 [IQR: 6-167] vs 214 [IQR: 57-560], P=0.009) and H-3c (2.32 [IQR: 0-19] vs 129 [IQR: 14-274], P=0.001). Conclusion. WWsurveillance is a powerful tool for early detection andmonitoring of circulating SARS-CoV-2VOCs.Total SARS-CoV-2 andVOC-specificWWsignal correlated with hospitalized prevalent cases of COVID-19 and outbreak occurrence.

Forecasting of COVID-19 confirmed cases in different countries with ARIMA models

The epidemic of a new coronavirus disease (COVID-19) has emerged as a global threat. Many countries and their health care systems were caught off guard. This study aims to predict the prevalence of COVID-19 in the most infected countries in the World Health Organization (WHO) regions in order to have better preparedness in health systems. The Auto-Regressive Integrated Moving Average (ARIMA) model was used to predict the pattern of confirmed cases based on epidemiological data from Johns Hopkins from February 25 to July 19, 2020. Mean incremental and logarithmic transfers were carried out to stabilize the series. Based on the ACF (AutoCorrelation Function) and PACF (Partial AutoCorrelation Function) charts, the first parameters of the model have been identified. The best model was chosen based on the likelihood ratio test and the least performance criteria value among all ARIMA models. Stata software version 12 was used. A number of ARIMA models have been formulated with various parameters. ARIMA (6,2,1) for South Africa, ARIMA (6,2,2) for U.S.A, ARIMA (2,1,1) for Iran, ARIMA (2,1,1) for Russia, ARIMA (5,2,2) for India, and ARIMA (3,1,2) for Australia were chosen based on the likelihood ratio tests and the values of the lower performance criteria. This research demonstrates that ARIMA models are sufficiently effective in predicting the prevalence of COVID-19 in the future. Predicting trends in COVID-19 prevalence in these countries can convince other countries to use this model in their future studies. The analysis results can help governments and health systems understand the patterns of this pandemic and plan for future waves of patients. Copyright © 2022 The Authors. Romanian Journal of Diabetes, Nutrition and Metabolic Diseases published by Sanatatea Press Group on behalf of the Romanian Society of Diabetes Nutrition and Metabolic Diseases.

Accurately Forecasting Pediatric Hematology and Oncology Patient Volumes at Treatment Centers in Sub-Saharan Africa

Background and Aims: Forecasting the number of new patients at pediatric hematology-oncology (PHO) treatment centers in resourceconstrained settings is important to optimize care delivery but can be difficult due to the month-to-month variability of the counts and changes in the expected number over time. We conducted a forecasting project to predict the number of new hematology and oncology patients at PHO treatment centers in Botswana (Botswana Treatment Center;BTC), Malawi (MTC), and Uganda (UTC) in 2021. Method(s): Monthly counts of new hematology and oncology patients were obtained between October 2016-March 2021. Time series models were fit to the data from Oct 2016-Dec 2020. Moving average and linear trend models were fit to the data with different time periods. The models were scored with validation data from January-March 2021 according to mean average error, root mean squared error, and lag-1 autocorrelation. The models with the top scores were chosen to forecast the mean and 95% prediction interval for Apr-Dec 2021. Differences between the observed and mean predicted (O-P) numbers of patients were calculated. Result(s): Linear trend models performed the best for all sites except for oncology patients at UTC where a moving average model that excluded the period of COVID-19 scored the best. The O-P differences for new oncology patients was 0(O-P;45-45) for BTC, +21(129-107) for MTC, and +14(186-172) for UTC. The difference for new hematology patients was +2(45-43) for BTC, +47(159-112) for MTC, -12(607-619) for UTC. The total forecast error for all sites was +71(1171-1100), or 6.1% of the total patients. All observed counts fell within the 95% prediction intervals except new hematology patients at MTC which fell outside the upper limit. Conclusion(s): Forecasting models from operations data can predict the total newly diagnosed patients at PHO treatment centers within an error of 6.1% of the observed total number of patients.