SIR-RL: Reinforcement Learning for Optimized Policy Control during Epidemiological Outbreaks in Emerging Market and Developing Economies (preprint)

The outbreak of COVID-19 has highlighted the intricate interplay between public health and economic stability on a global scale. This study proposes a novel reinforcement learning framework designed to optimize health and economic outcomes during pandemics. The framework leverages the SIR model, integrating both lockdown measures (via a stringency index) and vaccination strategies to simulate disease dynamics. The stringency index, indicative of the severity of lockdown measures, influences both the spread of the disease and the economic health of a country. Developing nations, which bear a disproportionate economic burden under stringent lockdowns, are the primary focus of our study. By implementing reinforcement learning, we aim to optimize governmental responses and strike a balance between the competing costs associated with public health and economic stability. This approach also enhances transparency in governmental decision-making by establishing a well-defined reward function for the reinforcement learning agent. In essence, this study introduces an innovative and ethical strategy to navigate the challenge of balancing public health and economic stability amidst infectious disease outbreaks.

Data rectification to account for delays in reporting disease incidence with an application to forecasting COVID-19 cases (preprint)

Effective monitoring of infectious disease incidence remains a major challenge to public health. Difficulties in estimating the trends in disease incidence arise mainly from the time delay between case diagnosis and the reporting of cases to public health databases. However, predictive models usually assume that public data sets faithfully reflect the state of disease transmission. In this paper, we study the effect of delayed case reporting by comparing data reported by the Johns Hopkins Coronavirus Resource Center (CRC) with that of the raw clinical data collected from the San Antonio Metro Health District (SAMHD), San Antonio, Texas. An insight on the subtle effect that such reporting errors potentially have on predictive modeling is presented. We use an exponential distribution model for the regression analysis of the reporting delay. The proposed model for correcting reporting delays was applied to our recently developed SEYAR (Susceptible, Exposed, Symptomatic, Asymptomatic, Recovered) dynamical model for COVID-19 transmission dynamics. Employing data from SAMHD, we demonstrate that the forecasting ability of the SEYAR model is substantially improved when the rectified reporting obtained from our proposed model is utilized. The methods and findings demonstrated in this work have ample applicability in the forecasting of infectious disease outbreaks. Our findings suggest that failure to consider reporting delays in surveillance data can significantly alter forecasts.

Quantifying the Regional Disproportionality of COVID-19 Spread (preprint)

BackgroundThe COVID-19 pandemic has caused serious health problems and has had major economic and social consequences worldwide. Understanding how infectious diseases spread can help mitigating the social and economic impact. ObjectiveThe study focuses to capture the degrees of disproportionality in prevalence rates of infectious disease across different regions over time. MethodsWe analyze the numbers of daily COVID-19 confirmed cases in the United States collected by Johns Hopkins University over 1100 days since the first reported case in January 2020 in order to assess quantitatively the disproportionality of the confirmed cases using the Theil index, a measure of imbalance used in economics. Results: Our results reveal a dynamic pattern of interregional disproportionality in the confirmed cases by monitoring variations in regional contributions to the Theil index as the pandemic progresses. ConclusionsThe combined monitoring of this indicator and the confirmed cases is crucial for understanding regional differences in infectious diseases and for effective planning of response and resource allocation.

Evaluating COVID-19 Surveillance Testing Strategies at Colorado School of Mines: A Stochastic Modeling Approach (preprint)

This study introduces a stochastic model of COVID-19 transmission tailored to the Colorado School of Mines campus and evaluates surveillance testing strategies within a university context. Enhancing the conventional SEIR framework with stochastic transitions, our model accounts for the unique characteristics of disease spread in a residential college, including specific states for testing, quarantine, and isolation. Employing an approximate Bayesian computation (ABC) method for parameter estimation, we navigate the complexities inherent in stochastic models, enabling an accurate fit of the model with the campus case data. We then studied our model under the estimated parameters to evaluate the efficacy of different testing policies that could be implemented on a university campus. This framework not only advances understanding of COVID-19 dynamics on the Mines campus but serves as a blueprint for comparable settings, providing insights for informed strategies against infectious diseases.

Exploration of the link between COVID-19 and gastric cancer from the perspective of bioinformatics and systems biology (preprint)

Background: Coronavirus disease 2019 (COVID-19), an infectious disease caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has caused a global pandemic. Gastric cancer (GC) poses a great threat to people's health, which is a high-risk factor for COVID-19. Previous studies have found some associations between GC and COVID-19, whereas the underlying molecular mechanisms are not well understood. Methods: We used a bioinformatics and systems biology approach to investigate the relationship between GC and COVID-19. The gene expression profiles of COVID-19 (GSE196822) and GC (GSE179252) were downloaded from the Gene Expression Omnibus (GEO) database. After identifying the shared differentially expressed genes (DEGs) for GC and COVID-19, functional annotation, protein-protein interaction (PPI) network, hub genes, transcriptional regulatory networks and candidate drugs were analyzed. Results: A total of 209 shared DEGs were identified to explore the linkages between COVID-19 and GC. Functional analyses showed that Immune-related pathway collectively participated in the development and progression of COVID-19 and GC. In addition, there are selected 10 hub genes including CDK1, KIF20A, TPX2, UBE2C, HJURP, CENPA, PLK1, MKI67, IFI6, and IFIT2. The transcription factor/gene and miRNA/gene interaction networks identified 38 transcription factors (TFs) and 234 miRNAs. More importantly, we identified ten potential therapeutic agents, including ciclopirox, resveratrol, etoposide, methotrexate, trifluridine, enterolactone, troglitazone, calcitriol, dasatinib and deferoxamine, some of which have been reported to improve and treat GC and COVID-19. This study also provides insight into the diseases most associated with mutual DEGs, which may provide new ideas for research on the treatment of COVID-19. Conclusions: This research has the possibility to be contributed to effective therapeutic in COVID-19 and GC.

Respiratory viral infection promotes the awakening and outgrowth of dormant metastatic breast cancer cells in lungs (preprint)

Breast cancer is the second most common cancer globally. Most deaths from breast cancer are due to metastatic disease which often follows long periods of clinical dormancy1. Understanding the mechanisms that disrupt the quiescence of dormant disseminated cancer cells (DCC) is crucial for addressing metastatic progression. Infection with respiratory viruses (e.g. influenza or SARS-CoV-2) is common and triggers an inflammatory response locally and systemically2,3. Here we show that influenza virus infection leads to loss of the pro-dormancy mesenchymal phenotype in breast DCC in the lung, causing DCC proliferation within days of infection, and a greater than 100-fold expansion of carcinoma cells into metastatic lesions within two weeks. Such DCC phenotypic change and expansion is interleukin-6 (IL-6)-dependent. We further show that CD4 T cells are required for the maintenance of pulmonary metastatic burden post-influenza virus infection, in part through attenuation of CD8 cell responses in the lungs. Single-cell RNA-seq analyses reveal DCC-dependent impairment of T-cell activation in the lungs of infected mice. SARS-CoV-2 infected mice also showed increased breast DCC expansion in lungs post-infection. Expanding our findings to human observational data, we observed that cancer survivors contracting a SARS-CoV-2 infection have substantially increased risks of lung metastatic progression and cancer-related death compared to cancer survivors who did not. These discoveries underscore the significant impact of respiratory viral infections on the resurgence of metastatic cancer, offering novel insights into the interconnection between infectious diseases and cancer metastasis.

Causes of death in people living with HIV in the post 95 -95 -95 era: Lessons from five AIDS Healthcare Foundation clinics in Eswatini (preprint)

Background Eswatini has a high HIV prevalence in adults (24.8%), and despite achieving HIV epidemic control, AIDS-related deaths are still high at 200 per 100,000 population. This study, therefore, describes the causes of death among people living with HIV (PLHIV) receiving care at five clinics in Eswatini. Methods Data of clients receiving antiretroviral therapy (ART) from five AIDS Healthcare Foundation (AHF) Clinics in Eswatini who died was analysed to describe the causes of death. Clients' records were included if they received treatment from any of the five clinics from January 1, 2021, to June 30, 2022. Clients' sociodemographic, clinical, and specific cause of death data were extracted from their clinical records into an Excel spreadsheet for mortality reporting and audits. The different causes of death were categorised and descriptive, and comparative analysis was done using Stata 15 and R. Odds ratio significant at p<0.05 (with 95% confidence interval) to estimate the different associations between the client's characteristics and the four leading causes of death. Results Of 257 clients, 52.5% (n=135) were males, and the median age was 47 years (IQR: 38, 59). The leading causes of death were non-communicable diseases (NCDs) (n=59, 23.0%), malignancies (n=37, 14.4%), Covid-19 (n=36, 14.0%) and advanced HIV disease (AHD) (n=24, 9.3%). Patients aged ≥60 years (OR 0.08; 95% CI: 0.004, 0.44) had lower odds of death from AHD than ≥40 years, and those who had been on ART for 12 – 60 months (OR 0.01; 95% CI: 0.0006, 0.06) and >60 months (OR 0.006; 95% CI: 0.0003, 0.029) had lower odds of death from AHD compared to those on ART for <12 months. Patients aged ≥40 years had higher odds of dying from COVID-19, while females (OR 2.64; 95% CI: 1.29, 5.70) had higher odds of death from malignancy. Conclusion Most patients who died were aged 40 years and above and died from an NCD, malignancy, COVID-19 and AHD-related cause. This indicates a need to expandprevention, screening, and integration of treatment for NCDs and cancers into HIV services. Specific interventions targeting younger PLHIV will limit their risks for AHD.

Nanoemulsions and nanocapsules as carriers for the development of intranasal mRNA vaccines (preprint)

The global emergency of coronavirus disease 2019 (COVID-19) has spurred extensive worldwide efforts to develop vaccines for protection against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Our contribution to this global endeavor involved the development of a diverse library of nanocarriers, as alternatives to lipid nanoparticles (LNPs), including nanoemulsions (NEs) and nanocapsules (NCs), with the aim of protecting and delivering messenger ribonucleic acid (mRNA) for nasal vaccination purposes. A wide range of prototypes underwent rigorous screening through a series of in vitro and in vivo experiments, encompassing assessments of cellular transfection, cytotoxicity, and intramuscular administration of a model mRNA for protein translation. Consequently, we identified two promising candidates for nasal administration. These candidates include an NE incorporating a combination of an ionizable lipid (C12-200) and cationic lipid (DOTAP) for mRNA entrapment, along with DOPE to facilitate endosomal escape. This NE exhibited a size of 120 nm and a highly positive surface charge (+50 mV). Additionally, an NC formulation comprising the same components with a dextran sulfate shell was identified, with a size of 130 nm and a moderate negative surface charge (-16 mV). Upon intranasal administration of mRNA encoding for ovalbumin (mOVA) associated with optimized versions of NEs and NCs, robust antigen-specific CD8+ T cell responses were observed. These findings underscore the potential of NEs and polymeric NCs in advancing mRNA vaccine development for combating infectious diseases.

Network-based time series modeling for COVID-19 incidence in the Republic of Ireland (preprint)

Network-based time series models have experienced a surge in popularity over the past years due to their ability to model temporal and spatial dependencies, arising from the spread of infectious disease.The generalised network autoregressive (GNAR) model conceptualises time series on the vertices of a network; it has an autoregressive component for temporal dependence and a spatial autoregressive component for dependence between neighbouring vertices in the network. Consequently, the choice of underlying network is essential.This paper assesses the performance of GNAR models on different networks in predicting COVID-19 cases for the 26 counties in the Republic of Ireland, over two distinct pandemic phases (restricted and unrestricted), characterised by inter-county movement restrictions.Ten static networks are constructed, in which vertices represent counties, and edges are built upon neighbourhood relations, such as railway lines. We find that a GNAR model based on the fairly sparse Queen's contiguity network explains the data best for the restricted pandemic phase while the fairly dense 21-nearest neighbour network performs best for the unrestricted phase.Across phases, GNAR models have higher predictive accuracy than standard ARIMA models which ignore the network structure. For county-specific predictions, in pandemic phases with more lenient or no COVID-19 regulation, the network effect is not quite as pronounced. The results indicate some robustness to the precise network architecture as long as the densities of the networks are similar.An analysis of the residuals justifies the model assumptions for the restricted phase but raises questions regarding their validity for the unrestricted phase.While outperforming ARIMA models which ignore network effects, the GNAR model warrants further development to better model complex infectious diseases, including COVID-19. 2020 Mathematics Subject Classification: 62M10, 05C82, 91D30 Network-based time series, COVID-19, spatial models, networks

Lessons from the COVID-19 pandemic to strengthen NCD care and policy in humanitarian settings: a mixed methods study exploring humanitarian actors’ experiences. (preprint)

Background The COVID-19 pandemic and response severely impacted people living with non-communicable diseases (PLWNCDs) globally. It exacerbated pre-existing health inequalities, severely disrupted access to care, and worsened clinical outcomes for PLWNCDs, who were at higher risk of morbidity and mortality from the virus. The pandemic’s effects were likely magnified in humanitarian settings, where there were pre-existing gaps in continuity of care for non-communicable diseases (NCDs). We sought to explore factors affecting implementation of NCD care in crises settings during the COVID-19 pandemic and the adaptations made to support implementation.Methods Guided by the Consolidated Framework for Implementation Research, we undertook an online survey of 98 humanitarian actors from multiple regions and organization types (March-July 2021), followed by in-depth interviews with 13 purposively selected survey respondents (October-December, 2021). Survey data were analysed using descriptive statistics, while interview data were analysed both deductively and inductively.Results Initially, humanitarian actors faced challenges influenced by external actors’ priorities, such as deprioritisation of NCD care by governments, travel restrictions and supply chain interruptions. With each infection wave and lockdown, humanitarian actors were better able to adapt and maintain NCD services. The availability of COVID-19 vaccines was a positive turning point, especially for the risk management of people with NCDs and protection of health workers. Key findings include that, despite pre-existing challenges, humanitarian actors largely continued NCD services during the crisis. Enabling factors that supported continuity of NCD services included the ability to quickly pivot to remote means of communication with PLWNCDs, flexibility in medicine dispensing, and successful advocacy to prioritize NCD management within health systems. Key lessons learned included the importance of partnerships and cooperation with other health actors and the mobilisation or repurposing of community health workers/volunteer networks.Conclusions The COVID-19 experience should prompt national and global health stakeholders to strengthen inclusion of NCDs in emergency preparedness, response, and resilience planning, building on lessons learned around remote care provision adapted to PLWNCDs severity, integrating community health workers, providing context-adapted PLWNCDs information and combating misinformation and strengthening cross-sectoral partnerships.

Genomic Surveillance of a Canadian Airport Wastewater Samples Allows Early Detection of Emerging SARS-CoV-2 Lineages (preprint)

The SARS-CoV-2 pandemic has shown that wastewater (WW) surveillance is an effective means of tracking the emergence of viral lineages in communities, arriving by many routes including via transportation hubs. In Ontario, numerous municipal WWTPs participate in WW surveillance of infectious disease targets such as SARS-CoV-2 by qPCR and whole genome sequencing (WGS). The Greater Toronto Airports Authority (GTAA), operator of Toronto Pearson International Airport (Toronto Pearson), has been participating in WW surveillance since January 2022. As a major international airport in Canada and the largest national hub, this airport is an ideal location for tracking globally emerging SARS-CoV-2 variants of concern (VOCs). In this study, WW collected from Toronto Pearson’s two terminals and pooled aircraft sewage was processed for WGS using a tiled-amplicon approach targeting the SARS-CoV-2 virus. Data generated was analyzed to monitor trends SARS-CoV-2 lineage frequencies. Initial detections of emerging lineages were compared between Toronto Pearson WW samples, municipal WW samples collected from the surrounding regions, and Ontario clinical data as published by Public Health Ontario. Results enabled the early detection of VOCs and individual mutations emerging in Ontario. On average, emergence of novel lineages at the airport ahead of clinical detections was 1–4 weeks, and up to 16 weeks. This project illustrates the efficacy of WW surveillance at transitory transportation hubs and sets an example that could be applied to other viruses as part of a pandemic preparedness strategy and to provide monitoring on a mass scale.

A Review of Graph Neural Networks in Epidemic Modeling (preprint)

Since the onset of the COVID-19 pandemic, there has been a growing interest in studying epidemiological models. Traditional mechanistic models mathematically describe the transmission mechanisms of infectious diseases. However, they often fall short when confronted with the growing challenges of today. Consequently, Graph Neural Networks (GNNs) have emerged as a progressively popular tool in epidemic research. In this paper, we endeavor to furnish a comprehensive review of GNNs in epidemic tasks and highlight potential future directions. To accomplish this objective, we introduce hierarchical taxonomies for both epidemic tasks and methodologies, offering a trajectory of development within this domain. For epidemic tasks, we establish a taxonomy akin to those typically employed within the epidemic domain. For methodology, we categorize existing work into \textit{Neural Models} and \textit{Hybrid Models}. Following this, we perform an exhaustive and systematic examination of the methodologies, encompassing both the tasks and their technical details. Furthermore, we discuss the limitations of existing methods from diverse perspectives and systematically propose future research directions. This survey aims to bridge literature gaps and promote the progression of this promising field. We hope that it will facilitate synergies between the communities of GNNs and epidemiology, and contribute to their collective progress.

Stakeholder Interviews to Inform Best Practice for Public Facing COVID-19 Wastewater Dashboards (preprint)

Background: WW-based epidemiology is the detection of pathogens from wastewater, typically sewage systems. Its use gained popularity during the COVID-19 pandemic as a rapid and non-invasive way to assess infection prevalence in a population. Public facing dashboards for SARS-CoV-2 were developed in response to the discovery that RNA biomarkers were being shed in faeces before symptoms. However, there is not a standard template or guidance for countries to follow. The aim of this research is to reflect on how currently available dashboards evolved during the pandemic and identify suitable content and rationale from these experiences. Methods and Results: Interviews were carried out with implementers and users of dashboards for SARS-CoV-2 WW data across Europe and North America. The interviews addressed commonalities and inconsistencies in displaying epidemiological data of SARS-CoV-2, clinical parameters of COVID-19, data on variants, and data transparency. The thematic analysis identified WW dashboard elements that can facilitate standardization, or at least interoperability. These elements emphasise communication among developers under the same organization, open access for identified stakeholders, and data summarized with a time-intensive graphic analysis through normalizing at least by population. Simultaneous communication of clinical surveillance is recommended. More research is needed on flow and faecal indicators for normalization of WW data, and on the analysis and representation of variants. Discussion: WW dashboard development between 2020-2023 provided a real-time iterative process of data representation, and several recommendations have been identified. Communication of data through dashboards has the potential to support early warning systems for infectious diseases.

Association between health systems and universal health coverage and COVID-19 testing rates in 194 countries: Cross-sectional ecological study (preprint)

Background The COVID-19 pandemic exposed the vulnerabilities of health systems worldwide, underscoring the critical need for robust and resilient healthcare infrastructure in effectively responding to disease outbreaks. This study aimed to address this need by examining the impact of health system indicators and universal health coverage (UHC) on COVID-19 testing rates.Method The study used secondary data from international organizations, peer-reviewed journals, and statistical portals. The primary outcome measure was COVID-19 testing rates, and the independent variables encompassed various health system indicators, including healthcare workforce density, the Healthcare Access and Quality Index (HAQ), UHC Coverage Index, and UHC Effective Coverage. The statistical analyses included simple and multiple regression models to determine the effects of these variables on COVID-19 testing rates while accounting for potential covariates.Findings: The findings revealed positive associations between health system indicators, UHC, and COVID-19 testing rates. Notably, the HAQ index exhibited the strongest positive correlation with COVID-19 testing rates. Multiple regression models further confirmed the positive relationships between UHC and health system indicators, and COVID-19 testing rates.Conclusion The study interpreted the results and underscored the significance of well-functioning health systems and UHC in achieving higher COVID-19 testing rates. This suggests that countries with well-structured health systems, advanced infrastructure, and adequate healthcare workforce are better equipped to conduct efficient testing. Moreover, the study emphasized the link between UHC and COVID-19 testing rates, noting that countries with greater UHC tend to exhibit higher testing rates. These findings contribute to our understanding of the association between health systems, UHC, and diagnostic testing for infectious diseases like COVID-19.

An early warning indicator trained on stochastic disease-spreading models with different noises (preprint)

The timely detection of disease outbreaks through reliable early warning signals (EWSs) is indispensable for effective public health mitigation strategies. Nevertheless, the intricate dynamics of real-world disease spread, often influenced by diverse sources of noise and limited data in the early stages of outbreaks, pose a significant challenge in developing reliable EWSs, as the performance of existing indicators varies with extrinsic and intrinsic noises. Here, we address the challenge of modeling disease when the measurements are corrupted by additive white noise, multiplicative environmental noise, and demographic noise into a standard epidemic mathematical model. To navigate the complexities introduced by these noise sources, we employ a deep learning algorithm that provides EWS in infectious disease outbreak by training on noise-induced disease-spreading models. The indicator's effectiveness is demonstrated through its application to real-world COVID-19 cases in Edmonton and simulated time series derived from diverse disease spread models affected by noise. Notably, the indicator captures an impending transition in a time series of disease outbreaks and outperforms existing indicators. This study contributes to advancing early warning capabilities by addressing the intricate dynamics inherent in real-world disease spread, presenting a promising avenue for enhancing public health preparedness and response efforts.

Wastewater-based Epidemiology for COVID-19 Surveillance: A Survey (preprint)

The pandemic of COVID-19 has imposed tremendous pressure on public health systems and social economic ecosystems over the past years. To alleviate its social impact, it is important to proactively track the prevalence of COVID-19 within communities. The traditional way to estimate the disease prevalence is to estimate from reported clinical test data or surveys. However, the coverage of clinical tests is often limited and the tests can be labor-intensive, requires reliable and timely results, and consistent diagnostic and reporting criteria. Recent studies revealed that patients who are diagnosed with COVID-19 often undergo fecal shedding of SARS-CoV-2 virus into wastewater, which makes wastewater-based epidemiology (WBE) for COVID-19 surveillance a promising approach to complement traditional clinical testing. In this paper, we survey the existing literature regarding WBE for COVID-19 surveillance and summarize the current advances in the area. Specifically, we have covered the key aspects of wastewater sampling, sample testing, and presented a comprehensive and organized summary of wastewater data analytical methods. Finally, we provide the open challenges on current wastewater-based COVID-19 surveillance studies, aiming to encourage new ideas to advance the development of effective wastewater-based surveillance systems for general infectious diseases.

Development of Prediction Models of COVID-19 Vaccine Uptake among Lebanese and Syrians in a district of Beirut, Lebanon: a population-based study (preprint)

ImportanceVaccines are essential to prevent infection and reduce morbidity of infectious diseases, and vulnerable populations may lack access to vaccination campaigns. Previous evidence has shown that migrants and refugees are particularly vulnerable to exclusion, stigma and discrimination, and low COVID-19 vaccine intention and uptake were observed among refugees globally. ObjectiveTo develop and internally validate prediction models of COVID-19 vaccine uptake by nationality. DesignThis is a nested prognostic population-based cross-sectional analysis. SettingData was collected between June and October 2022 in Sin-El-Fil, a district of Beirut, Lebanon. ParticipantsAll Syrian adults and a random sample of other adults from low-socioeconomic status neighborhoods were invited to participate in the study (n=3,138). A telephone survey with consenting participants (n=2,045) was conducted. ExposuresCandidate predictors of COVID-19 vaccine uptake identified from the literature were collected. Main Outcome and MeasuresThe main outcome was uptake of COVID-19 vaccine. Predictors of COVID-19 vaccine uptake were assessed using LASSO regression for Lebanese and Syrian nationalities, respectively. The models discrimination capabilities are presented using the AUC, and their calibration are presented using the calibration slopes. ResultsOf 2,045 participants, 79% were Lebanese, 18% Syrians and 3% of other nationalities. COVID-19 vaccination uptake was higher among Lebanese (85% (95%CI:82-86) compared to Syrians (47% (95% CI:43-51)) (P<0.001); adjusted odds ratio (aOR) 6.8 (95%CI:5.5-8.4). Predictors of uptake of one or more vaccine dose for Lebanese were older age, presence of an older adult in the household, higher education, greater asset-based wealth index, private healthcare coverage, feeling susceptible to COVID-19, belief in the safety and efficacy of vaccines and previous receipt of flu vaccine. For Syrians they were older age, male, completing school or higher education, receipt of cash assistance, presence of comorbidities, belief in the safety and efficacy of vaccines, previous receipt of flu vaccine, and legal residency status in Lebanon. Conclusions and RelevanceThese findings indicate barriers for vaccine uptake in Syrian migrants and refugees, including legal residency status. They call for urgent action to enable equitable access to vaccines by raising awareness about the importance of vaccination and the targeting of migrant and refugee populations through vaccination campaigns. FundingInternational Development Research Centre (IDRC) - Canada Key PointsO_ST_ABSQuestionC_ST_ABSWhat are the context-specific differential predictors of vaccine uptake among the resident population by nationality in Lebanon? FindingsThis is a nested population-based cross-sectional analysis that examined predictors of COVID-19 vaccine uptake among Lebanese and Syrians living in low-socioeconomic neighborhoods in 2022. Socioeconomic, demographic and health risk perceptions were identified for both Syrians and Lebanese, while additional barriers identified in Syrian refugees included legal residency status in the country. MeaningDespite the availability of vaccines to all residents, there were inequalities in vaccine uptake between Syrians and Lebanese which need to be addressed.

Optimal prevention strategies for chronic diseases in a compartmental disease trajectory model (preprint)

In countries with growing elderly populations, multimorbidity poses a significant healthcare challenge. The trajectories along which diseases accumulate as patients age and how they can be targeted by prevention efforts are still not fully understood. We propose a compartmental model, traditionally used in infectious diseases, describing chronic disease trajectories across 132 distinct multimorbidity patterns (compartments). Leveraging a comprehensive dataset from approximately 45 million hospital stays spanning 17 years in Austria, our compartmental disease trajectory model (CDTM) forecasts changes in the incidence of 131 diagnostic groups and their combinations until 2030, highlighting patterns involving hypertensive diseases with cardiovascular diseases and metabolic disorders. We pinpoint specific diagnoses with the greatest potential for preventive interventions to promote healthy aging. According to our model, a reduction of new onsets by 5% of hypertensive diseases (I10-I15) leads to a reduction in all-cause mortality over a period of 15 years by 0.57 (0.06)% and for malignant neoplasms (C00-C97) mortality is reduced by 0.57 (0.07)%. Furthermore, we use the model to assess the long-term consequences of the Covid-19 pandemic on hospitalizations, revealing earlier and more frequent hospitalizations across multiple diagnoses. Our fully data-driven approach identifies leverage points for proactive preparation by physicians and policymakers to reduce the overall disease burden in the population, emphasizing a shift towards patient-centered care.

Epidemiological and transmission characteristics comparison analysis between Delta and Omicron variants of the SARS-CoV-2 (BA.2.1, BA.2.2, BA.2.76, BA.5.2) (preprint)

Since the outbreak of coronavirus disease 2019 (COVID-19), the virus has undergone three mutations, with Delta and Omicron being the most affected.This study aimed to understand the epidemiology and transmission differences between the Delta and Omicron variants, and to analyze the infection characteristics of different variants, providing a scientific theoretical basis for prevention and control strategies.We conducted a comparative analysis by selecting six local outbreaks of the Delta variant that occurred in Hunan Province in July 2021 and six local outbreaks of different sub-lineages of the Omicron variant that occurred in 2022. The results showed that asymptomatic cases were more prevalent in Omicron variant infections, with BA.5.2 having the highest proportion. The Delta and Omicron variants have identical median incubation periods of 2–3 days. In terms of secondary situations, the secondary attack rate of the Delta variant is 0.85%, while that of the Omicron variant is 1.69%. For specific Omicron subvariants, Omicron BA.2.1 has a secondary attack rate of 0.89%, Omicron BA.2.2 is 0.71%, Omicron BA.2.76 is 2.51%, and Omicron BA.5.2 has a secondary attack rate of 4.63%. The predominant mode of exposure for cases with recurrent infections of the Delta variant is cohabitation, while for Omicron variant outbreaks, cohabitation remains predominant, followed by spatial proximity and dining together.The Delta variant and the Omicron variant are both make it prone to causing multiple generations of cases in a short period, leading to a wider impact. The secondary attack rates of Omicron and Delta variants in this study were much lower than in other countries, indicating that strengthening personnel control and social regulations are beneficial for the prevention and control of newly emerging severe infectious diseases. Meanwhile, the exposure types of Omicron variant secondary cases were more diverse, and the symptoms of infected individuals were milder, indicating its greater stealthiness. Therefore, it is crucial to focus on virus mutations, strengthen surveillance, and increase prevention and control efforts if enhanced transmissibility of the variant is detected.

Time-Since-Infection Model for Hospitalization and Incidence Data (preprint)

The Time Since Infection (TSI) models, which use disease surveillance data to model infectious diseases, have become increasingly popular recently due to their flexibility and capacity to address complex disease control questions. However, a notable limitation of TSI models is their primary reliance on incidence data. Even when hospitalization data are available, existing TSI models have not been crafted to estimate disease transmission or predict disease-related hospitalizations - metrics crucial for understanding a pandemic and planning hospital resources. Moreover, their dependence on reported infection data makes them vulnerable to variations in data quality. In this study, we advance TSI models by integrating hospitalization data, marking a significant step forward in modeling with TSI models. Our improvements enable the estimation of key infectious disease parameters without relying on contact tracing data, reduce bias in incidence data, and provide a foundation to connect TSI models with other infectious disease models. We introduce hospitalization propensity parameters to jointly model incidence and hospitalization data. We use a composite likelihood function to accommodate complex data structure and an MCEM algorithm to estimate model parameters. We apply our method to COVID-19 data to estimate disease transmission, assess risk factor impacts, and calculate hospitalization propensity.