BackgroundCOVID-19 forecasting models have been used to inform decision-making around resource allocation and intervention decisions, such as hospital beds or stay-at-home orders. State-of-the-art forecasting models often use multimodal data, including mobility or sociodemographic data, to enhance COVID-19 case prediction models. Nevertheless, related work has revealed under-reporting bias in COVID-19 cases as well as sampling bias in mobility data for certain minority racial and ethnic groups, which affects the fairness of COVID-19 predictions across racial and ethnic groups.ObjectiveThis study aims to introduce a fairness correction method that works for forecasting COVID-19 cases at an aggregate geographic level.MethodsWe use hard and soft error parity analyses on existing fairness frameworks and demonstrate that our proposed method, Demographic Optimization (DemOpts), performs better in both scenarios.ResultsWe first demonstrate that state-of-the-art COVID-19 deep learning models produce mean prediction errors that are significantly different across racial and ethnic groups at larger geographic scales. We then propose a novel debiasing method, DemOpts, to increase the fairness of deep learning–based forecasting models trained on potentially biased datasets. Our results show that DemOpts can achieve better error parity than other state-of-the-art debiasing approaches, thus effectively reducing the differences in the mean error distributions across racial and ethnic groups.ConclusionsWe introduce DemOpts, which reduces error parity differences compared with other approaches and generates fairer forecasting models compared with other approaches in the literature.

To examine whether electronic health record (EHR) workload for primary care and other physicians was associated with increases in COVID-19 cases by region of the United States (US). Retrospective data analysis of Epic EHR workload measures for almost 500,000 outpatient physicians and other physicians across the US from May 2019 to May 2022. The association of COVID-19 disease rates on time in the EHR varied by specialty. For primary care physicians, increases in regional disease prevalence were associated with significant increases in the time spent in the In Basket as well as "pajama time" (time outside of scheduled work hours); for other specialties, increases in COVID rates were associated with smaller increases in In Basket time and some region-specific decreases in pajama time. For all participants, regardless of specialty, overall EHR workload increased over the course of the pandemic. Increases in COVID-19 cases were associated with increased EHR workload for outpatient physicians across the US, with the greatest impact on primary care physicians performing asynchronous patient care tasks. These findings capture the experience of almost half a million physicians and illuminate how mitigating burnout from a global pandemic likely also extends to efforts to reduce EHR workload. Our results show direct impacts of COVID-19 rates on physician workloads, particularly in primary care, and can hopefully inform future efforts to manage workload should another pandemic occur.

Purpose This study aims to optimize pharmaceutical emergency logistics under dynamic demand and disrupted routes during public health crises. By integrating multi-scenario analysis and multimodal transportation, it seeks to minimize response time, unmet demand penalties, and costs while balancing efficiency and equity. The model addresses limitations of traditional single-mode logistics, leveraging COVID-19 case data to enhance adaptability in resource allocation. Design/methodology/approach A robust optimization model is developed, integrating dynamic demand forecasting, scenario probabilities, and capacity constraints across four epidemic stages. The NSGA-III algorithm is employed to solve multi-objective trade-offs, with performance compared against NSGA-II using metrics like spacing and Pareto ratio. Robust standard vectors and scenario probabilities are analyzed to evaluate stability, supported by computational experiments from Chinese cities like Wuhan. Findings NSGA-III outperformed NSGA-II, generating 60% more Pareto solutions in T4 with 3% faster computation. Robust vectors significantly influenced outcomes: γ3 increased penalty costs linearly in high-demand phases, while γ1 escalated procurement expenses over time. Scenario probabilities p3 reduced penalties by 15–20% through coordinated logistics. Practical implications The framework enables emergency managers to prioritize air transport for urgent deliveries and establish centralized hubs, reducing average response times by 18%. Public-private partnerships and dynamic inventory adjustments improve equity and efficiency, particularly in high-risk regions. Originality/value This study contributes to the field by unifying dynamic demand modelling, multimodal transport optimization, and robust scenario-based decision-making into a single analytical framework. The application of NSGA-III effectively resolves many-objective optimization challenges, outperforming traditional methods in both diversity and convergence. A scenario-driven parameter analysis is introduced to quantitatively assess the impacts of uncertainty, thereby advancing theory in crisis logistics management.

The scale-up of molecular assays for diagnosing emerging pathogens has increased in low-and-middle-income countries (LMICs) since the advent of COVID-19. We herein evaluated the diagnostic concordance of three different assays for SARS-CoV-2 in Cameroon. A laboratory-based comparative study was performed on nasopharyngeal samples collected between March-2020 to March-2023 from the biobank of Chantal Biya International Reference Centre (CIRCB), Yaoundé-Cameroon. Samples were analyzed using DaAn Gene (N/ORF1ab-genes), ThermoFisher (N/ORF1ab/S-genes), and GeneXpert (N2/E-genes). Validated cycle thresholds (CT) for positivity were CT < 37 for DaAn Gene/ThermoFisher and CT < 40 for GeneXpert. Cohen's Kappa coefficient evaluated diagnostic concordance with DaAn Gene as reference. We analysed 249 samples (55.8% males, median-age [IQR], 36 [27-50] years including 21.3% symptomatic participants). Overall positivity rates (median [IQR]) were 55.0% (CT: 30.6 [23.1-35.5]); 53.4% (CT: 26.6 [21.2-30.9]); 22.1% (CT: 32.7 [26.9-36.1]) for GeneXpert, DaAn Gene and ThermoFisher respectively. GeneXpert showed stronger concordance with DaAn Gene (83.1%; k = 0.66, 95% CI: 0.57-0.75) than ThermoFisher (67.9%; k = 0.38, 95% CI: 0.29-0.47). At validated thresholds, GeneXpert showed higher positive agreement with DaAn Gene (85.0%, 113/133) as compared to ThermoFisher (41.3%, 55/133), while maintaining comparable negative agreement (81.0% [GeneXpert] and 98.3% [ThermoFisher]). At low CTs (< 20) however, positive agreement with DaAn Gene was high for GeneXpert (100%, 15/15) and ThermoFisher (93.3%, 14/15). GeneXpert exhibits superiority over ThermoFisher in detecting cases of COVID-19. As expected, agreement between two- and three-genes assays at CT < 20 was excellent, suggesting interoperability of these platforms during outbreaks for high viral loads cases. However, two-genes assays may be decisive to guide decision-making for effective public health response while facing intermediate to low-level viral loads in LMICs.

The infodemic-pandemic nexus: Cross-national analysis of online misinformation's impact on population health.

A toy optimization model for understanding the decision-making against a pandemic: COVID-19 case study

This study aimed to detect the changes in certain immunological parameters and miRNAs in COVID-19 cases with various degrees of disease severity and compare these changes in cases to healthy controls. This study was conducted on 45 COVID-19 patients and 45 healthy controls. The flow cytometry was conducted to study the number of CD4 + and CD8 + T cells and evaluate the level of the PD-1 marker on their surfaces for all study participants. The determination of IL-1β and IL-6 in serum for all study subjects was done by ELISA test. Relative gene expression quantitation of miR-146a and miR-133a was performed by reverse transcriptase real-time PCR (RT-PCR). The numbers of CD4 + and CD8 + T cells were dramatically reduced in COVID-19 patients, especially in severe to critical patients, with an increase in the CD4 + :CD8 + ratio. T cells from COVID-19 patients had significantly higher levels of the exhausted marker PD-1. Measurement of IL-1β and IL-6 serum levels among cases group showed a highly significant increase in their mean concentration levels in comparison with the control group. Studying the difference in serum levels of IL-1β and IL-6 among different degrees of disease severity showed a significant decrease in their mean concentration levels among the mild to moderate group in comparison with the severe to critical group. The results also showed a significant decrease in miR-146a and a significant increase in miR-133a expression in COVID-19 patients compared to healthy controls. Reduced T cell counts, increased CD4 + :CD8 + ratio, higher levels of the PD-1 marker, elevated serum levels of the pro-inflammatory cytokines, and decreased miR-146a and increased miR-133a gene expressions could be used as potential markers in the assessment of COVID-19 infection and severity.

This descriptive ecological study analyzed clinical and sociodemographic data and the geospatial distribution of COVID-19 cases and deaths in Mato Grosso (MT) between March 2020 and March 2023, obtained from the Indica-SUS system. The local empirical Bayesian method and GeoDa 1.20 software were applied to identify risk patterns and spatial distribution. The average incidence was 24,089 cases/100,000 inhabitants, with a mortality rate of 4.1/100,000 and a lethality rate of 1.7%. Of the 879,706 cases and 15,091 deaths recorded, 63.1% of deaths occurred by 2021. The infection was higher in females (54.1%), brown-skinned individuals (64.9%), aged 30-39 years (22.6%), while the higher lethality in males (58.1%), with hypertension (25.5%) and diabetes (43.3%). The cities with the highest number of notifications were Cuiabá (17.0%), Várzea Grande (6.8%), and Sinop (4.7%). Three risk clusters for new cases (RR: 2.05–3.92) were identified in 97 municipalities and five clusters of death risk (RR: 1.14–2.48) in 27 municipalities. The highest prevalence of infection occurred between March and August 2020, in the north and east of the state. The risk of new cases was higher in the East and Southwest, while deaths were concentrated in the Southwest, South, and Southeast. Although detection was higher among females, mortality was concentrated among males with comorbidities, highlighting the greater clinical vulnerability of this group.

Introduction: India reported over 30 million confirmed COVID-19 cases and nearly 400000 deaths. The nationwide lockdown beginning on March 25, 2020, and prolonged campus closures led to remote-learning, restricted mobility, and limited access to healthy foods among students. Understanding these lifestyle changes in health-professional students who will guide future patients is essential. Objective: To assess changes in physical activity, dietary behaviours, and body mass index (BMI) among undergraduate health students before, during, and after the COVID-19 lockdown. Methods: A follow-up observational study was conducted among final-year MBBS, BDS, BPT, and BPharm students at a tertiary medical university in South India. All eligible students (N=550) were approached; 470 consented. Pre-lockdown weight and height were obtained by recall, while weight and height were measured during lockdown and weight re-measured after lockdown (September 2021 onwards). Physical activity was assessed using the International Physical Activity Questionnaire–Short Form (IPAQ-SF), and dietary behaviours with a pre-tested semi-structured questionnaire. Descriptive statistics, paired t-tests, repeated measures ANOVA, and multiple linear regression were performed. Results: Mean BMI remained unchanged from pre- to during lockdown but decreased significantly afterwards, while still slightly exceeding baseline values (p &lt; 0.001). Physical activity (MET-min/week) declined significantly during lockdown compared with baseline (mean difference –364.78, p &lt; 0.001). Stress eating (40%), night-time eating (34.3%), and increased junk food intake (24.9%) were reported. Regression analysis identified physical activity change (β = –0.252, p &lt; 0.001) and fitness app use (β = –0.219, p = 0.017) as protective against BMI gain, whereas increased meal frequency predicted higher BMI (β = 0.088, p = 0.048). Model explained 8.7% of variance in BMI change. Conclusion: COVID-19 lockdown led to reduced physical activity and altered dietary behaviours among health-professional students, producing modest but sustained BMI increases. Student-focused health-promotion strategies and digital tools to maintain activity are warranted.

Background : Multisystem Inflammatory Syndrome in Children (MIS-C) is a rare but severe complication following SARS-CoV-2 infection. Early recognition and forecasting may support preparedness and clinical resource allocation. Objective : We aimed to model the temporal relationship between COVID-19 incidence and MIS-C cases using time series analysis. Materials and methods : We retrospectively analyzed data from 51 children with MIS-C (age 0–18 years) hospitalized in a single center between November 2020 and April 2021. A regression model with ARIMA(1,0,0) errors was applied to predict MIS-C cases based on weekly COVID-19 incidence in individuals aged 0–18 years. Results : The majority of children were male (n = 37, 72.5%) and older than 5 years (n = 40, 78%), with a mean age of 8.82 ± 4.16 years. Abdominal symptoms were predominant (n = 38, 74%), and nearly half experienced respiratory (n = 23, 45%) or cardiac (n = 26, 51%) involvement. The ARIMA model identified a lag of 5–6 weeks between COVID-19 cases and MIS-C incidence (R² = 0.41). A selection-aware Monte Carlo simulation confirmed the robustness of the model’s significant regressors when extrapolated beyond the original data period. Conclusion : The moderate predictive power of the ARIMA model supports the potential of syndromic surveillance to forecast MIS-C clusters, aiding early intervention. Larger datasets could further enhance prediction accuracy.

Seguimiento sintomatológico a largo plazo de pacientes con síndrome de COVID persistente

Since the end of 2019, a novel coronavirus known as COVID-19 has caused a severe outbreak worldwide. Due to the complexity of epidemic data, traditional algorithms have struggled to accurately predict the development of the pandemic. The Autoregressive Integrated Moving Average (ARIMA) model is capable of capturing time-based trends in epidemic data, including seasonality, cyclic patterns, and long-term trends, which helps improve the accuracy of forecasting future epidemic trajectories. The Bidirectional Long Short-Term Memory (BiLSTM) network, a variant of the Recurrent Neural Network (RNN), is highly effective in handling sequential data. In epidemic data analysis, BiLSTM models can be applied to forecast future trends or conduct time series predictions. BiLSTM is able to capture temporal relationships and sequential patterns within data, thereby providing more accurate predictions. Genetic Algorithms (GA), inspired by biological evolution through operations such as selection, crossover, and mutation, offer an efficient approach to identifying the best-fit models and parameter configurations. By using GA, we can iteratively optimize epidemic forecasting models and enhance their performance over time. In this study, we proposed a hybrid model called GA-BiLSTM-ARIMA. Using COVID-19 case data from Japan, we calculated the GA-BiLSTM-ARIMA model's evaluation metrics: RMSE, MAE, MAPE, and R2, which were 2,262.42, 1,672.07, 6.81, and 0.9764, respectively. The results demonstrate that the hybrid model outperforms both the standalone BiLSTM and ARIMA models in predictive performance. The GA-BiLSTM-ARIMA model successfully integrates the strengths of different models through a systematic and intelligently optimized hybrid strategy. When forecasting infectious disease time series data, this model achieves higher and more robust predictive accuracy compared to traditional single models or partial hybrid models. This type of analysis supports the development of more effective prevention and control strategies and delivers accurate information and early warnings to the public and policymakers, contributing to a better global response to pandemic challenges.

BackgroundThe COVID-19 pandemic has drawn attention to the interconnected roles of environmental conditions and public health beyond conventional medical explanations. The One Health (OH) perspective offers a collaborative and interdisciplinary perspective that integrates humans, animals, plants, and their shared environment to achieve optimal health outcomes. The 12 cities in Hubei Province that experienced lockdown during the peak phase of COVID-19 (February 1 to March 4, 2020) provided unique samples. In this study, land cover was selected as the environmental variable, and the COVID-19 growth rate was used as the infectious disease indicator to examine their relationship, thereby investigating the potential role of environmental factors in epidemic control.MethodsThe Least Absolute Shrinkage and Selection Operator (LASSO) regression was used to identify the most influential variables for subsequent analyses. Spatial autocorrelation was assessed using Moran’s I in RStudio, while spatial dependence was explicitly modeled through the Spatial Autoregressive (SAR) and Spatial Lag of X (SLX) models to evaluate the effects of explanatory variables while accounting for spatial interactions. All results were interpreted within the One Health perspective, considering the source of infection, routes of transmission, and susceptible populations.ResultsLASSO regression identified wetland, cultivated land, orchard land, forest land, and population density as the main factors associated with the COVID-19 growth rate. Wetland coverage exhibited a significant positive association with growth rate, whereas cultivated land showed a negative but marginally significant relationship. Orchard land and forest land were associated with weak negative effects.ConclusionThe statistical results indicate that variations in land cover influence the growth rate of COVID-19 cases, suggesting that environmental management, including wetland and wastewater control, agricultural landscape configuration, forest vegetation preservation, and control population density, may help mitigate infectious disease growth. From the One Health perspective, sustainable habitat design and planning strategies and land use policies were proposed for future research.

BackgroundIt is well established from trials that BP telemonitoring leads to improved BP control. However, there is little data available on the impact of BP telemonitoring on the incidence of cardiovascular events when it is used as the routine mode of long term BP monitoring,AimThis study aimed to explore the impact of Blood Pressure (BP) telemonitoring on clinical cardiovascular outcomes, including during the covid-19 pandemic when the service was being provided with little face to face support.MethodRecords were analysed for 442097 adults with hypertension identified from prescribing records from 5 Scottish Health Boards.Patients were included in they had a prescription for a first line anti-hypertensive drug at any time from 1 March 2019 (1 year before the first cases of COVID-19 were identified in Scotland) and 28 February 2021. Follow up was until 1st March 2022. Women pregnant during that time were excluded.The primary outcome measure was emergency hospital admission for Acute Coronary Syndrome (ACS), Stroke or uncontrolled Heart Failure (HF). Outcomes were compared between people who had used BP telemonitoring for at least 1 year and a matched group who had not used it at all. Matching was on age, sex, ethnicity, social deprivation, number of anti-hypertensive drugs, diabetes and having a BP assessment in the same year.ResultsNinety percent of the cohort had been diagnosed with hypertension before March 2019, 7% between March 2019 and February 2020 and only 3% in the first year of the covid-19 pandemic.There was a rapid increase in the uptake of BP telemonitoring after the start of the covid-19 pandemic. Those who used telemonitoring were significantly younger, less likely to have diabetes and take less antihypertensive medication. For those who used telemonitoring for over 1 year a mean reduction in systolic BP was seen which was maintained for at least the remainder of the year.In the matched group analysis people who used telemonitoring were less likely than those who were not to be admitted to hospital with or die from ACS, stroke or uncontrolled heart failure (adjusted OR 0.498 (95% CI 0.336 to 0.739), p=0.001), or to die through any cause (adjusted OR 0.484 (95% CI 0.268 to 0.875), p=0.016), p=0.018) or be admitted to hospital for any cause (adjusted OR 0.713 (95% CI 0.629 to 0.809), p<0.001).DiscussionThe strength of this study is that, for the first time, enough people were using BP telemonitoring as a long term routine service for the effect on clinical outcomes to be measured. It demonstrated that the reduction in systolic BP achieved at the start of telemonitoring was maintained and was associated with a significant reduction in cardiovascular events. However the study took place at a time of service disruption and longer term evaluation, with access to BP records for those not using telemonitoring, is needed.

This study aims to investigate how demographic variables - specifically age, gender and income - influenced community perceptions of decentralized governance leadership during the COVID-19 crisis in Elavally Panchayat, Kerala. It explores residents' varied experiences with local service delivery and support mechanisms, revealing disparities in satisfaction and access. The findings underscore the importance of inclusive, adaptive and crisis-responsive local leadership during public health emergencies. Adopting a mixed-methods approach, this study combined unstructured interviews with elected local self-government leaders and primary health centre staff with a structured household survey. The survey included first 100 household respondents drawn from confirmed COVID-19 cases across all 16 wards. Six dimensions of governance performance were assessed using a three-point Likert scale (Satisfied, Neutral and Dissatisfied). Descriptive statistics and chi-square tests (p < 0.05) were applied to examine demographic variations in satisfaction levels. Qualitative insights from interviews further contextualized and deepened the findings. The results indicate significant demographic disparities in perceived leadership effectiveness. Younger adults appreciated digital welfare schemes but felt excluded from local decision-making. Middle-aged residents experienced severe healthcare and livelihood disruptions, while elderly individuals struggled with digital health systems. Women expressed higher satisfaction due to targeted welfare schemes, whereas men cited unmet mental health and economic needs. Lower-income groups reported barriers to accessing essential services. Nonetheless, decentralized governance grounded in trust, equity and participatory approaches enabled an effective, context-specific crisis response. Unlike studies that treat communities as homogeneous, this research disaggregates perceptions, offering policy insights to strengthen equity and resilience in local health governance.

Introduction: Laboratory waste management (LWM) is an important precondition to safeguard the healthcare workers (HCWs), community members and the environment, from contamination with infections. Worldwide, at least 5.2 million people die each year from diseases originating from poorly managed medical waste. Managing the Laboratory waste during pandemics as exemplified by COVID-19 requires critical attention to mitigate the transmission of the pathogenic agent. This study aimed at assessing LWM in the context of COVID-19 pandemic in the Buea Health District. Methods: A prospective study was conducted from March to June 2022. A checklist was used to record observations whilst structured questionnaires were used to get information from HCWs. Descriptive analysis (age, sex, profession category, educational status and work experience) was carried out. Bivariate analyses and multivariable logistic regression were used to identify predictor variables for waste management practices of HCWs. Adjusted Odds Ratio at 95% confidence level was used to measure the strength of association. P-value &lt; 0.05 was considered for statistical significance. Results: The proportion of HCWs who had good level of knowledge, good attitude and precautions in LWM were 48.4%, 84.8% and 65.0% respectively. Medical Laboratory Technicians (60.8%) and age group 18-30 years (64.0%) got training on Laboratory waste management. Segregation and disinfection reduced the spread of the disease (88.4%), and Laboratory technicians with good attitudes (50.4%) significantly took precautions in handling Laboratory waste in government hospitals. Conclusion: Less than half of HCWs had good knowledge on LWM. Hence, in-service training is recommended to improve LWM knowledge and practices.

The first case of COVID-19 in Burkina Faso was reported in March 2020. As of June 8, 2025, Burkina Faso reported 22,114 confirmed cases and 400 deaths. However, few studies have investigated the spatiotemporal dynamics of pandemics within the national boundaries. This study provides a retrospective spatial analysis of COVID-19 transmission in Burkina Faso and identifies the key geographic drivers. Case statistics from March 2020 to December 2021 were sourced from the Directorate of Health Information Systems of the Ministry of Health. Covariates were identified through a literature review and retrieved from local and online resources. Spatial and temporal patterns were analyzed using ArcGIS Pro® 3.4.3. Hotspots and directional trends were mapped using Getis-Ord Gi* statistics and standard deviation ellipses, and district-level spatial associations were evaluated. Multiscale Geographically Weighted Regression (MGWR) was used to model the relationships between disease incidence and geographic features. Five major transmission phases were observed. Specifically, 20 Health Districts were affected between March and April 2020, 38 in September 2020, 62 in April 2021, and 67 in December 2021. Initially, a single hotspot centered in Ouagadougou was identified. A second hotspot emerged in Bobo Dioulasso in September 2020, considerable heterogeneity in case distribution was noted across the districts. The MGWR results highlight population density, poverty rate, relative wealth index, and distance to testing centers as the main spatial drivers, collectively explaining 70% of the variance in incidence. The findings revealed a fast-evolving outbreak with significant spatial variation, revealed the need for adaptive, geography-informed responses. This multiphase framework can inform real-time risk forecasting and improve epidemic preparednessin in low-resource settings.

Effect of cold spells and heatwaves on daily new cases of COVID-19 in the Western Pacific Region: A time series analysis.

Health and economic burden of COVID-19 in Malaysia between January 2020 and December 2022.

Introduction: The COVID-19 pandemic is challenging due to its high transmissibility and mortality rates. COVID-19 patients can rapidly deteriorate, underscoring the need to identify lab biomarkers for high-risk categorization. This study aims to explore the role and correlation of various laboratory parameters, including Neutrophil-to-Lymphocyte Ratio (NLR), Ferritin, Prothrombin (PT), D-Dimer, C-reactive protein (CRP), and Procalcitonin (PCT), in distinguishing between severe and non-severe cases of COVID-19. Materials and methods: This retrospective cross-sectional study was carried out at Sulianti Saroso Infectious Disease Hospital in Jakarta with approval from the ethics committee. The inclusion criteria for subjects consist of patients confirmed with COVID-19 through PCR test results, adults aged over 18 years, and those with relevant laboratory parameter results. The exclusion criteria include pregnant patients, patients who arrive in a state of death on arrival (DOA), and patients with incomplete data. A sample of 1,598 adult COVID-19 patients was analysed. Laboratory data were extracted from electronic medical records (SIMINTRO) from March 2020 to December 2022. The significance of the means was assessed through the independent Mann-Whitney test, with a p-value &lt;0.05 regarded as statistically significant. After constructing the ROC (receiver-operating characteristic) curve, threshold values were identified based on Youden's index (J). Result: There are differences in the severe and non-severe groups based on age, gender, transmission risk factors, symptoms, and comorbidities (p&lt;0.05). Severe COVID-19 patients show markedly elevated levels of (NLR, Ferritin, Prothrombin, D-Dimer, CRP, and Procalcitonin) compared to non-severe ones, and the statistical cut-off values between severe and non-severe groups according to parameters (NLR, Ferritin, PT, D-Dimer, CRP, and PCT) are significant (p&lt;0.001). Conclusion: Besides clinical findings, biochemical parameters are valuable predictors for assessing COVID-19 severity.