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Epidemics and Pandemics (disease outbreaks) are the occurrence of cases of disease in excess of what would be normally expected. Epidemic-prone diseases, including emerging and re-emerging diseases, constitute the greatest threat to public health security and disruption of social and economic development. When outbreaks are diagnosed in specific areas, an outbreak response is triggered to stop the spread rapidly. In the past 20 years, the sub-Saharan region has witnessed a marked increase in the number of outbreaks in pandemics, such as cholera, dengue, A/H5N 1 influenza among others. While efforts toward containment have been individually studied, we have no recent studies that examine them collectively in order to draw appropriate comparisons, no recent studies that have especially focused on hard-to-reach areas, and none that have applied a health systems lens. This study thus details a scoping review of short-term health system responses to epidemics across hard-to-reach areas in sub-Saharan Africa. The scoping review will be undertaken following PRISMA guidelines. A modified Donabedian framework will be used to understand the different approaches used while responding to epidemics. The review will focus on published and unpublished studies that report short-term health systems responses to epidemics in hard to reach areas. These will be gleaned from PubMed, google scholar and Cochrane, supplemented by a Google advanced search. In addition, manual searches will be carried out through related articles and websites. Data will be charted, coded, and narratively synthesized. our exclusion criteria will include; protocols, book chapters and countries not identified as hard to reach areas in SSA. We anticipate developing a document that will show the different approaches health systems in different countries used when responding to epidemics. The information generated will contribute to strengthening future epidemic responses by identifying best practices and innovative ideas as well as highlighting knowledge gaps. [ABSTRACT FROM AUTHOR]

In this research, we developed an epidemic model with a combination of Atangana-Baleanu Caputo derivative and classical operators for the hybrid operator's memory effects, allowing us to observe the dynamics and treatment effects at different time phases of syphilis infection caused by sex. The developed model properties, which take into account linear growth and Lipschitz requirements relating the rate of effects within its many sub-compartments according to the equilibrium points, include positivity, unique solution, exitance, and boundedness in the feasible domain. After conducting sensitivity analysis with various parameters influencing the model for the piecewise fractional operator, the reproductive number R0 for the biological viability of the model is determined. Generalized Ulam-Hyers stability results are employed to preserve global stability. The investigated model thus has a unique solution in the specified subinterval in light of the Banach conclusion, and contraction as a consequence holds for the Atangana-Baleanu Caputo derivative with classical operators. The piecewise model that has been suggested has a maximum of one solution. For numerical solutions, piecewise fractional hybrid operators at various fractional order values are solved using the Newton polynomial interpolation method. A comparison is also made between Caputo operator and the piecewise derivative proposed operator. This work improves our knowledge of the dynamics of syphilis and offers a solid framework for assessing the effectiveness of interventions for planning and making decisions to manage the illness. [ABSTRACT FROM AUTHOR]

Network disruption is pivotal in understanding the robustness and vulnerability of complex networks, which is instrumental in devising strategies for infrastructure protection, epidemic control, cybersecurity, and combating crime. In this paper, with a particular focus on disrupting criminal networks, we proposed to impose a within-the-largest-connected-component constraint in a continuous batch removal disruption process. Through a series of experiments on a recently released Sicilian Mafia network, we revealed that the constraint would enhance degree-based methods while weakening betweenness-based approaches. Moreover, based on the findings from the experiments using various disruption strategies, we propose a structurally-filtered greedy disruption strategy that integrates the effectiveness of greedy-like methods with the efficiency of structural-metric-based approaches. The proposed strategy significantly outperforms the longstanding state-of-the-art method of betweenness centrality while maintaining the same time complexity. [ABSTRACT FROM AUTHOR]

Despite much progress in exploring the coupled epidemic-awareness dynamics in multiplex networks, little attention has been paid to the joint impacts of behavioral observability and social proof on epidemic spreading. Since both the protective actions taken by direct neighbors and the observability of these actions have essential influence on individuals' decisions. Thus, we propose a UAPU-SIR model by integrating the effects of these two factors into the decision-making process of taking preventive measures. Specifically, a new state called taken protective actions is introduced into the original unaware-aware-unaware (UAU) model to characterize the action-taken state of individuals after getting epidemic-related information. Using the Microscopic Markov Chain Approach (MMCA), the methods and model are described, and the epidemic threshold is analytically derived. We find that both observability of protecting behaviors and social proof can reduce the epidemic prevalence and raise the epidemic threshold. Moreover, only if observability of protection actions reaches a certain threshold can accelerating information diffusion is able to inhibit disease spreading and result in higher epidemic threshold. We also discover that, reducing the forgetting rate of information is able to decrease epidemic size. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic and influenza outbreaks have underscored the critical need for predictive models that can effectively integrate spatial and temporal dynamics to enable accurate epidemic forecasting. Traditional time-series analysis approaches have fallen short in capturing the intricate interplay between these factors. Recent advancements have witnessed the incorporation of graph neural networks and machine learning techniques to bridge this gap, enhancing predictive accuracy and providing novel insights into disease spread mechanisms. Notable endeavors include leveraging human mobility data, employing transfer learning, and integrating advanced models such as Transformers and Graph Convolutional Networks (GCNs) to improve forecasting performance across diverse geographies for both influenza and COVID-19. However, these models often face challenges related to data quality, model transferability, and potential overfitting, highlighting the necessity for more adaptable and robust approaches. This paper introduces the Graph Attention-based Spatial Temporal (GAST) model, which employs graph attention networks (GATs) to overcome these limitations by providing a nuanced understanding of epidemic dynamics through a sophisticated spatio-temporal analysis framework. Our contributions include the development and validation of the GAST model, demonstrating its superior forecasting capabilities for influenza and COVID-19 spread, with a particular focus on short-term, daily predictions. The model's application to both influenza and COVID-19 datasets showcases its versatility and potential to inform public health interventions across a range of infectious diseases. [ABSTRACT FROM AUTHOR]

To address the epidemic, such as COVID-19, the government may implement the home quarantine policy for the infected residents. The logistics company is required to control the risk of epidemic spreading while delivering goods to residents. In this case, the logistics company often uses vehicles and unmanned aerial vehicles (UAVs) for delivery. This paper studies the distribution issue of cold chain logistics by integrating UAV logistics with epidemic risk management innovatively. At first, a "vehicle-UAV" joint distribution mode including vehicles, small UAVs and large UAVs, is proposed. The green cost for vehicles and UAVs is calculated, respectively. The formula for infection risk due to large numbers of residents gathering at distribution centers to pick up goods is then derived. Furthermore, based on the control of infection risk, an optimization model is developed to minimize the total logistics cost. A modified ant colony algorithm is designed to solve the model. The numerical results show that the maximum acceptable risk and the crowd management level of distribution centers both have significant effects on the distribution network, logistics cost and number of new infections. Our study provides a new management method and technical idea for ensuring the needs of residents during the epidemic. [ABSTRACT FROM AUTHOR]

The effective reproduction number, Rt , is an important epidemiological metric used to assess the state of an epidemic, as well as the effectiveness of public health interventions undertaken in response. When Rt is above one, it indicates that new infections are increasing, and thus the epidemic is growing, while an Rt is below one indicates that new infections are decreasing, and so the epidemic is under control. There are several established software packages that are readily available to statistically estimate Rt using clinical surveillance data. However, there are comparatively few accessible tools for estimating Rt from pathogen wastewater concentration, a surveillance data stream that cemented its utility during the COVID-19 pandemic. We present the R package ern that aims to perform the estimation of the effective reproduction number from real-world wastewater or aggregated clinical surveillance data in a user-friendly way. [ABSTRACT FROM AUTHOR]

Our study conducts a comprehensive analysis of the Covid-19 pandemic in Brazil, spanning five waves over three years. We employed a novel Susceptible-Infected-Recovered-Dead-Susceptible (SIRDS) model with a fuzzy transition between epidemic periods to estimate time-varying parameters and evaluate case underreporting. The initial basic reproduction number (R0) is identified at 2.44 (95% Confidence Interval (CI): 2.42–2.46), decreasing to 1.00 (95% CI: 0.99–1.01) during the first wave. The model estimates an underreporting factor of 12.9 (95% CI: 12.5–13.2) more infections than officially reported by Brazilian health authorities, with an increasing factor of 5.8 (95% CI: 5.2–6.4), 12.9 (95% CI: 12.5–13.3), and 16.8 (95% CI: 15.8–17.5) in 2020, 2021, and 2022 respectively. Additionally, the Infection Fatality Rate (IFR) is initially 0.88% (95% CI: 0.81%–0.94%) during the initial phase but consistently reduces across subsequent outbreaks, reaching its lowest value of 0.018% (95% CI: 0.011–0.033) in the last outbreak. Regarding the immunity period, the observed uncertainty and low sensitivity indicate that inferring this parameter is particularly challenging. Brazil successfully reduced R0 during the first wave, coinciding with decreased human mobility. Ineffective public health measures during the second wave resulted in the highest mortality rates within the studied period. We attribute lower mortality rates in 2022 to increased vaccination coverage and the lower lethality of the Omicron variant. We demonstrate the model generalization by its application to other countries. Comparative analyses with serological research further validate the accuracy of the model. In forecasting analysis, our model provides reasonable outbreak predictions. In conclusion, our study provides a nuanced understanding of the Covid-19 pandemic in Brazil, employing a novel epidemiological model. The findings contribute to the broader discourse on pandemic dynamics, underreporting, and the effectiveness of health interventions. [ABSTRACT FROM AUTHOR]

Since 8th March 2020 up to the time of writing, we have been producing near real-time weekly estimates of SARS-CoV-2 transmissibility and forecasts of deaths due to COVID-19 for all countries with evidence of sustained transmission, shared online. We also developed a novel heuristic to combine weekly estimates of transmissibility to produce forecasts over a 4-week horizon. Here we present a retrospective evaluation of the forecasts produced between 8th March to 29th November 2020 for 81 countries. We evaluated the robustness of the forecasts produced in real-time using relative error, coverage probability, and comparisons with null models. During the 39-week period covered by this study, both the short- and medium-term forecasts captured well the epidemic trajectory across different waves of COVID-19 infections with small relative errors over the forecast horizon. The model was well calibrated with 56.3% and 45.6% of the observations lying in the 50% Credible Interval in 1-week and 4-week ahead forecasts respectively. The retrospective evaluation of our models shows that simple transmission models calibrated using routine disease surveillance data can reliably capture the epidemic trajectory in multiple countries. The medium-term forecasts can be used in conjunction with the short-term forecasts of COVID-19 mortality as a useful planning tool as countries continue to relax public health measures.

COVID-19 has been a massive trade shock that has disrupted global trade, making the last few years a special phase. Even during normal times, epidemic diseases have acted as trade shocks in specific countries, albeit not to the same extent as COVID-19. For some trade shocks, the situation normalizes after the disease transmission is over; for some, it does not. Thus, specific countries can sometimes lose their original trade ratio due to trade diversion; that is, an epidemic disease could lead to unexpected industry restructuring. To examine this, based on data on 110 WHO members from 1996 to 2018, we use a fixed-effect panel model supported by the Hausman Test to empirically identify whether epidemic diseases can cause trade shocks and trade diversion. We find: First, epidemic disease can lead to negative shocks to a country's trade growth and its ratio of worldwide trade. Second, with a longer epidemic, the probability of the trade diversion effect increases. Our results hold even after considering country heterogeneity. This presents a considerable concern about the shock of COVID-19 lasting further. Many countries are not just facing the problem of temporary trade shocks, but also the challenge of trade diversions. In particular, the probability of trade diversions is increasing rapidly, especially for late-developed countries due to their lack of epidemic containment and vaccine-producing capabilities. Even middle and high income countries cannot ignore global industry chain restructuring. Forward-looking policies should be implemented in advance; it may be too late when long-term trade damage is shown. [ABSTRACT FROM AUTHOR]

Background: The fatality rate is a crucial metric for guiding public health policies during an ongoing epidemic. For COVID-19, the age structure of the confirmed cases changes over time, bringing a substantial impact on the real-time estimation of fatality. A 'spurious decrease' in fatality rate can be caused by a shift in confirmed cases towards younger ages even if the fatalities remain unchanged across different ages. Methods: To address this issue, we propose a standardized real-time fatality rate estimator. A simulation study is conducted to evaluate the performance of the estimator. The proposed method is applied for real-time fatality rate estimation of COVID-19 in Germany from March 2020 to May 2022. Findings: The simulation results suggest that the proposed estimator can provide an accurate trend of disease fatality in all cases, while the existing estimator may convey a misleading signal of the actual situation when the changes in temporal age distribution take place. The application to Germany data shows that there was an increment in the fatality rate at the implementation of the 'live with COVID' strategy. Conclusions: As many countries have chosen to coexist with the coronavirus, frequent examination of the fatality rate is of paramount importance. [ABSTRACT FROM AUTHOR]

Introduction: The traditional approach to epidemic control has been to slow down the rate of infection while building up healthcare capacity, resulting in a flattened epidemic curve. Advancements in bio-information-communication technology (BICT) have enabled the preemptive isolation of infected cases through efficient testing and contact tracing. This study aimed to conceptualize the BICT-enabled epidemic control (BICTEC) and to document its relationships with epidemic curve shaping and epidemic mitigation performance. Methods: Daily COVID-19 incidences were collected from outbreak to Aug. 12, 2020, for nine countries reporting the first outbreak on or before Feb. 1, 2020. Key epidemic curve determinants–peak height (PH), time to peak (TTP), and area under the curve (AUC)–were estimated for each country, and their relationships were analyzed to test if epidemic curves peak quickly at a shorter height. CFR (Case Fatality Rate) and CI (Cumulative Incidence) were compared across the countries to identify relationships between epidemic curve shapes and epidemic mitigation performance. Results: China and South Korea had the quickest TTPs (40.70 and 45.37 days since outbreak, respectively) and the shortest PHs (2.95 and 4.65 cases per day, respectively). Sweden, known for its laissez-faire approach, had the longest TTP (120.36) and the highest PH (279.74). Quicker TTPs were correlated with shorter PHs (ρ = 0·896, p = 0·0026) and lower AUCs (0.790, p = 0.0028), indicating that epidemic curves do not follow a flattened trajectory. During the study period, countries with quicker TTPs tended to have lower CIs (ρ =.855, P =.006) and CFRs (ρ = 0.684, P =.061). For example, South Korea, with the second-quickest TTP, reported the second lowest CI and the lowest CFR. Conclusions: Countries that experienced early COVID-19 outbreaks demonstrated the epidemic curves that quickly peak at a shorter height, indicating a departure from the traditional flattened trajectory. South Korea's BICTEC was found to be at least as effective as most lockdowns in reducing CI and CFR. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has been present globally for more than three years, and cross-border transmission has played an important role in its spread. Currently, most predictions of COVID-19 spread are limited to a country (or a region), and models for cross-border transmission risk assessment remain lacking. Information on imported COVID-19 cases reported from March 2020 to June 2022 was collected from the National Health Commission of China, and COVID-19 epidemic data of the countries of origin of the imported cases were collected on data websites such as WHO and Our World in Data. It is proposed to establish a prediction model suitable for the prevention and control of overseas importation of COVID-19. Firstly, the SIR model was used to fit the epidemic infection status of the countries where the cases were exported, and most of the r2 values of the fitted curves obtained were above 0.75, which indicated that the SIR model could well fit different countries and the infection status of the region. After fitting the epidemic infection status data of overseas exporting countries, on this basis, a SIR-multiple linear regression overseas import risk prediction combination model was established, which can predict the risk of overseas case importation, and the established overseas import risk model overall P <0.05, the adjusted R2 = 0.7, indicating that the SIR-multivariate linear regression overseas import risk prediction combination model can obtain better prediction results. Our model effectively estimates the risk of imported cases of COVID-19 from abroad. [ABSTRACT FROM AUTHOR]

Background: The purpose of this paper is to explore the relationship between subjective well-being, social class identity, and Self-rated health among older persons,. Focusing on the mediating role of health and the impact of epidemic infectious diseases on these relationships. Methods: Based on the 2018 and 2021 China General Social Survey (CGSS) databases, the data were screened, and processed. Using Stata17, we employed ordered probit regression to examine the relationships among variables and Bootstrap methods to assess mediation effects, and the CGSS data for 2018 and 2021 were compared and analyzed. Results: Our results revealed that factors such as social class identity, health status, and personal income significantly positively impact older persons' subjective well-being (P<0.01). Notably, there was a partial mediating effect of health status between the subjective well-being of the elderly and social class identity. And findings showed that when older adults were affected by epidemic diseases, their subjective well-being, social class identity, and Self-rated health remained significantly positively correlated. Subjective well-being, social class identity. What is more noteworthy is that when affected by epidemic infectious diseases, older adults' subjective well-being, social class identity, and Self-rated health remained significantly positively correlated. The mediating role of self-rated health in older adults' subjective well-being and social class identity increased from 9.6% to 12.4%. Conclusions: In the face of epidemic infectious diseases, we need to pay more attention to the Self-rated health of the elderly, and the Chinese government should take effective measures to improve their health level, which will in turn improve the subjective well-being of the elderly and realize the goal of healthy aging. [ABSTRACT FROM AUTHOR]

The World Health Organization (WHO) raised the global alert level for the A(H1N1) influenza pandemic in June 2009. However, since the beginning of the epidemic, the fight against the epidemic lacked foundations for managing cases to reduce the disease lethality. It was urgent to carry out studies that would indicate a model for predicting severe forms of influenza. This study aimed to identify risk factors for severe forms during the 2009 influenza epidemic and develop a prediction model based on clinical epidemiological data. A case-control of cases notified to the health secretariats of the states of Rio de Janeiro, São Paulo, Minas Gerais, Paraná, and Rio Grande do Sul was conducted. Cases had fever, respiratory symptoms, positive confirmatory test for the presence of the virus associated with one of the three conditions: (i) presenting respiratory complications such as pneumonia, ventilatory failure, severe acute respiratory distress syndrome, sepsis, acute cardiovascular complications or death; or respiratory failure requiring invasive or non-invasive ventilatory support, (ii) having been hospitalized or (iii) having been admitted to an Intensive Care Unit. Controls were individuals diagnosed with the disease on the same date (or same week) as the cases. A total of 1653 individuals were included in the study, (858 cases/795 controls). These participants had a mean age of 26 years, a low level of education, and were mostly female. The most important predictors identified were systolic blood pressure in mmHg, respiratory rate in bpm, dehydration, obesity, pregnancy (in women), and vomiting (in children). Three clinical prediction models of severity were developed, for adults, adult women, and for children. The performance evaluation of these models indicated good predictive capacity. The area values under the ROC curve of these models were 0.89; 0.98 and 0.91 respectively for the model of adults, adult women, and children respectively. [ABSTRACT FROM AUTHOR]

Assessing public health intervention strategies is crucial for effectively managing dengue. While numerous studies have explored the impact of dengue interventions on its transmission dynamics, limited research has focused on the combined effects of implementing multiple therapeutic interventions for disease control. This study presents an epidemic model for understanding dengue transmission dynamics, incorporating two critical therapeutic measures: vaccination and treatment of infected individuals. The model is characterized by ordinary differential equations involving seven-state variables. The investigation encompasses both disease-free and endemic equilibria of the model. The findings reveal that the disease-free equilibrium (only) is globally stable when the basic reproduction number is below one. Interestingly, when the vaccine's effectiveness is low, treatment emerges as a more successful approach in reducing dengue cases than vaccination. In contrast, a highly effective vaccine alone significantly curtails dengue occurrences. Moreover, the study introduces an optimal control problem, featuring an objective function integrating two control mechanisms: vaccination and treatment. The analysis strongly suggests that implementing two control strategies outweighs the efficacy of a single approach in effectively mitigating the spread of the disease. [ABSTRACT FROM AUTHOR]

Objectives: The COVID-19 pandemic showed distinct waves where cases ebbed and flowed. While each country had slight, nuanced differences, lessons from each wave with country-specific details provides important lessons for prevention, understanding medical outcomes and the role of vaccines. This paper compares key characteristics from the five different COVID-19 waves in Pakistan. Methods: Data was sourced from daily national situation reports (Sitreps) prepared by the National Emergency Operations Centre (NEOC) in Islamabad. We use specific criteria to define COVID-19 waves. The start of each COVID-19 wave is marked by the day of the lowest number of daily cases preceding a sustained increase, while the end is the day with the lowest number of cases following a 7-days decline, which should be lower than the 7 days following it. Key variables such as COVID-19 tests, cases, and deaths with their rates of change to the peak and then to the trough are used to draw descriptive comparisons. Additionally, a linear regression model estimates daily new COVID-19 deaths in Pakistan. Results: Pakistan saw five distinct waves, each of which displayed the typical topology of a complete infectious disease epidemic. The time from wave-start to peak became progressively shorter, and from wave-peak to trough, progressively longer. Each wave appears to also be getting shorter, except for wave 4, which lasted longer than wave 3. A one percent increase in vaccinations decreased deaths by 0.38% (95% CI: -0.67, -0.08) in wave 5 and the association is statistically significant. Conclusion: Each wave displayed distinct characteristics that must be interpreted in the context of the level of response and the variant driving the epidemic. Key indicators suggest that COVID-19 preventive measures kept pace with the disease. Waves 1 and 2 were mainly about prevention and learning how to clinically manage patients. Vaccination started late during wave 3 and its impact on hospitalizations and deaths became visible in wave 5. The impact of highly virulent strains Alpha/B.1.1.7 and Delta/B.1.617.2 variants during wave 3 and milder but more infectious Omicron/B.1.1.529 during wave 5 are apparent. [ABSTRACT FROM AUTHOR]

Influenza is an acute respiratory infectious disease that commonly affects people and has an important impact on public health. Based on influenza incidence data from 103 counties in Hubei Province from 2009 to 2019, this study used time series analysis and geospatial analysis to analyze the spatial and temporal distribution characteristics of the influenza epidemic and its influencing factors. The results reveal significant spatial-temporal clustering of the influenza epidemic in Hubei Province. Influenza mainly occurs in winter and spring of each year (from December to March of the next year), with the highest incidence rate observed in 2019 and an overall upward trend in recent years. There were significant spatial and urban-rural differences in influenza prevalence in Hubei Province, with the eastern region being more seriously affected than the central and western regions, and the urban regions more seriously affected than the rural region. Hubei's influenza epidemic showed an obvious spatial agglomeration distribution from 2009 to 2019, with the strongest clustering in winter. The hot spot areas of interannual variation in influenza were mainly distributed in eastern and western Hubei, and the cold spot areas were distributed in north-central Hubei. In addition, the cold hot spot areas of influenza epidemics varied from season to season. The seasonal changes in influenza prevalence in Hubei Province are mainly governed by meteorological factors, such as temperature, sunshine, precipitation, humidity, and wind speed. Low temperature, less rain, less sunshine, low wind speed and humid weather will increase the risk of contracting influenza; the interannual changes and spatial differentiation of influenza are mainly influenced by socioeconomic factors, such as road density, number of health technicians per 1,000 population, urbanization rate and population density. The strength of influenza's influencing factors in Hubei Province exhibits significant spatial variation, but in general, the formation of spatial variation of influenza in Hubei Province is still the result of the joint action of socioeconomic factors and natural meteorological factors. Understanding the temporal and spatial distribution characteristics of influenza in Hubei Province and its influencing factors can provide a reasonable decision-making basis for influenza prevention and control and public health development in Hubei Province and can also effectively improve the scientific understanding of the public with respect to influenza and other respiratory infectious diseases to reduce the influenza incidence, which also has reference significance for the prevention and control of influenza and other respiratory infectious diseases in other countries or regions. [ABSTRACT FROM AUTHOR]

Introduction: Obesity is a growing and debilitating epidemic worldwide that is associated with an increased inflammation. It is often linked to rheumatic diseases and may impact negatively their natural history. The use of bariatric and metabolic surgery (BMS) has increased thanks to its positive effect on major comorbidities like diabetes type 2. This systematic review provides the most up-to-date published literature regarding the effect of BMS on outcomes in rheumatoid arthritis. Methods: This systematic review followed the preferred reporting items for systematic reviews guidelines. Original articles from Pubmed, Embase and Cochrane, published until June 16th 2023, and tackling the effect of BMS on disease outcomes in patients with RA were included. Results: Three studies met the inclusion criteria. They were published between 2015 and 2022. The total number of RA patients was 33193 and 6700 of them underwent BMS. Compared to non-surgical patients, weight loss after BMS was associated with lower disease activity outcomes at 12 months (p<0.05). Similarly, prior BMS in RA patients was significantly associated with reduced odds ratios for all the morbidities and in-hospital mortality compared with no prior BMS (36.5% vs 54.6%, OR = 0.45, 95% CI (0.42, 0.48), p< 0.001) and (0.4% vs 0.9%, OR = 0.41, 95% CI (0.27–0.61), p < 0.001) respectively. Conclusion: To conclude, published data indicate that BMS seems a promising alternative in reducing RA disease activity as well as morbidity and mortality in patients with obesity. [ABSTRACT FROM AUTHOR]

Background: The "Ending the HIV Epidemic" (EHE) initiative seeks to reduce new HIV infections in the U.S. by prioritizing federal resources towards highly impacted populations. Antiretroviral therapy (ART) and pre-exposure prophylaxis (PrEP) are essential for reaching EHE goals. Adolescents are often at increased risk for HIV because they may lack agency in negotiating their sexual partnerships and may not have the same access to treatment and prevention as adults. This study estimates the potential contribution of expanded PrEP coverage among adolescents ages 15–17 to achieving the EHE goals in the South. Methods: An HIV-transmission model was built to simulate the HIV epidemic in the South. Increased ART and PrEP uptake were systematically varied with and without PrEP eligibility including individuals age<18. Results: Prioritizing PrEP for adolescents had a negligible impact on incidence. At 50% uptake among eligible adolescents and 90% ART coverage, including adolescents only improved the percentage of infections averted from 80.1% to 80.3%. In 10 of 15 scenarios explored, there was no reduction in new infections when PrEP eligibility was expanded to include adolescents age<18. At 95% ART coverage at the population-level incidence among adolescents declined by over 80%, but PrEP uptake among adolescents did not contribute to additional declines in incidence among adolescents. Conclusions: Prioritizing PrEP for adolescents did not significantly contribute to reaching EHE incidence reductions goal. Focusing resources to specific adolescent populations at risk, such sexual minority males in high incidence settings, will remain an important public health goal outside the context of EHE. [ABSTRACT FROM AUTHOR]

We propose an algorithm to simulate Markovian SIS epidemics with homogeneous rates and pairwise interactions on a fixed undirected graph, assuming a distributed memory model of parallel programming and limited bandwidth. This setup can represent a broad class of simulation tasks with compartmental models. Existing solutions for such tasks are sequential by nature. We provide an innovative solution that makes trade-offs between statistical faithfulness and parallelism possible. We offer an implementation of the algorithm in the form of pseudocode in the Appendix. Also, we analyze its algorithmic complexity and its induced dynamical system. Finally, we design experiments to show its scalability and faithfulness. In our experiments, we discover that graph structures that admit good partitioning schemes, such as the ones with clear community structures, together with the correct application of a graph partitioning method, can lead to better scalability and faithfulness. We believe this algorithm offers a way of scaling out, allowing researchers to run simulation tasks at a scale that was not accessible before. Furthermore, we believe this algorithm lays a solid foundation for extensions to more advanced epidemic simulations and graph dynamics in other fields. [ABSTRACT FROM AUTHOR]

The urgency to develop vaccines during the COVID-19 pandemic has resulted in the acceleration of clinical trials. Specifically, a broad spectrum of efficacy levels has been reported for various vaccines based on phase III cohort studies. Our study demonstrates that conducting large cohort phase III clinical trials during the peak of an epidemic leads to a significant underestimation of vaccine efficacy, even in the absence of confounding factors. Furthermore, we find that this underestimation increases with the proportion of infectious individuals in the population during the experiment and the severity of the epidemic, as measured by its basic reproduction number. [ABSTRACT FROM AUTHOR]

In recent years, while the relationship between the new financial institutions, represented by financial technology companies, and the traditional financial institutions(banks, securities, insurance, etc.) has been steadily enhanced, a New Relational Network has silently emerged. Along with the rapid expansion of big fintech companies, the possibility of financial risk breeding and spreading in the New Relational Network is also rising. This article analyzes and simulates the risk contagion mechanism of big fintech risks based on the SIRS epidemic model. The study's findings imply that: when the number of big fintech companies infected with risk exceeds the risk threshold, the big fintech risk will spread in the New Relational Network. At this time, the number of big fintech companies infected with risk can be reduced below the threshold by enhancing the risk warning, risk management, risk buffering and blocking capabilities, and timely improving risk prevention and control measures in the post-infection phase. It means that the big fintech risk is controlled. For big fintech risks, proactive interventions are more effective than post-incident response measures. This paper makes the following recommendations for preventing big fintech risks: creating a risk monitoring and early warning system to raise the Big Fintech companies' direct immunization rates; strengthening the big fintech companies' risk management and risk mitigation capabilities; enhancing the internal and external supervision to achieve sustainable development of big fintech companies. [ABSTRACT FROM AUTHOR]

BackgroundEpidemics of COVID-19 in student populations at universities were a key concern for the 2020-2021 school year. The University of California (UC) System developed a set of recommendations to reduce campus infection rates. SARS-CoV-2 test results are summarized for the ten UC campuses during the Fall 2020 term.MethodsUC mitigation efforts included protocols for the arrival of students living on-campus students, non-pharmaceutical interventions, daily symptom monitoring, symptomatic testing, asymptomatic surveillance testing, isolation and quarantine protocols, student ambassador programs for health education, campus health and safety pledges, and lowered density of on-campus student housing. We used data from UC campuses, the UC Health-California Department of Public Health Data Modeling Consortium, and the U.S. Census to estimate the proportion of each campus' student populations that tested positive for SARS-CoV-2 and compared it to the fraction individuals aged 20-29 years who tested positive in their respective counties.ResultsSARS-CoV-2 cases in campus populations were generally low in September and October 2020, but increased in November and especially December, and were highest in early to mid-January 2021, mirroring case trajectories in their respective counties. Many students were infected during the Thanksgiving and winter holiday recesses and were detected as cases upon returning to campus. The proportion of students who tested positive for SARS-CoV-2 during Fall 2020 ranged from 1.2% to 5.2% for students living on campus and was similar to students living off campus. For most UC campuses the proportion of students testing positive was lower than that for the 20-29-year-old population in which campuses were located.ConclusionsThe layered mitigation approach used on UC campuses, informed by public health science and augmented perhaps by a more compliant population, likely minimized campus transmission and outbreaks and limited transmission to surrounding communities. University policies that include these mitigation efforts in Fall 2020 along with SARS-CoV-2 vaccination, may alleviate some local concerns about college students returning to communities and facilitate resumption of normal campus operations and in-person instruction.

Background: Toxoplasmosis is a zoonotic parasitic disease caused by the protozoan Toxoplasma gondii (T. gondii), and may cause miscarriage and birth defects during pregnancy. This study aimed to assess the epidemiological features, epidemic trends, and correlations between the before number of confirmed toxoplasmosis cases in Taiwan from 2007 to 2020 in gender, age, season, and residential area, and hypothesized the environmental and climate factors also might affect the disease in Taiwan. Methods: This study reviewed publicly available annual summary data on reported toxoplasmosis cases in the Taiwan Centers for Diseases Control (TCDC) between 2007 and 2020. Results: This study collected 150 confirmed domestic and nine patients with imported toxoplasmosis. There was an increasing trend in the incidence of toxoplasmosis, 0.09–0.89 cases per 1,000,000 people, peaking in 2017. The average annual toxoplasmosis incidence was 4.4, 13, and 18 during 2007–2011, 2012–2016, and 2017–2020, respectively. Comparing sex, age, season, and place of residence, the incidence rate was highest in male, 20–39 years-old patients, summer, and the eastern region, with 1.02, 1.72, 0.38, and 3.63 cases per million population, respectively. Additionally, comparing the distribution of cases by age group in Taiwan, there were significant differences between 40–59 years-old in the northern region (odds ratio (OR) = 0.343, 95% confidence interval (CI) = 0.167–0.705, p = 0.004) and 40–59 years-old in the southern region (OR = 4.488, and 95% CI = 2.149–9.374, p< 0.001), respectively. Linear regression analysis also showed that PM (particulate matter) 2.5 (μg/m3) concentration was positively associated with toxoplasmosis (β = 0.095, p = 0.037). CO concentration was negatively correlated with toxoplasmosis (β = -14.001, p = 0.021). Conclusions: This study is the first to report domestic and confirmed cases of imported toxoplasmosis from the surveillance data of the TCDC between 2007 and 2020. It identified that residence and age were associated with an increased risk of toxoplasmosis in Taiwan. This study confirmed that toxoplasmosis remains a prevalent infectious disease in Taiwan, its epidemic is gradually increasing and becoming more severe. These findings might be useful for policy-makers and clinical experts to direct prevention and control activities to patients with T. gondii, which causes the most severe illness and greatest burden to Taiwanese people. [ABSTRACT FROM AUTHOR]

This paper studies the updated estimation method for estimating the transmission rate changes over time. The models for the population dynamics under SEIR epidemic models with stochastic perturbations are analysed the dynamics of the COVID-19 pandemic in Bogotá, Colombia. We performed computational experiments to interpret COVID-19 dynamics using actual data for the proposed models. We estimate the model parameters and updated their estimates for reported infected and recovered data. [ABSTRACT FROM AUTHOR]

The outbreak and prevalence of SARS-CoV-2 have severely affected social security. Physical isolation is an effective control that affects the short-term human-to-human transmission of the epidemic, although weather presents a long-term effect. Understanding the effect of weather on the outbreak allow it to be contained at the earliest possible. China is selected as the study area, and six weather factors that receive the most attention from January 20, 2020 to April 30, 2020 are selected to investigate the correlation between weather and SARS-CoV-2 to provide a theoretical basis for long-term epidemic prevention and control. The results show that (1) the average growth rate (GR) of SARS-CoV-2 in each province is logarithmically distributed with a mean value of 5.15%. The GR of the southeastern region is higher than that of the northwestern region, which is consistent with the Hu Line. (2) The specific humidity, 2-m temperature (T), ultraviolet (UV) radiation, and wind speed (WS) adversely affect the GR. By contrast, the total precipitation (TP) and surface pressure (SP) promote the GR. (3) For every 1 unit increase in UV radiation, the GR decreases by 0.30% in 11 days, and the UV radiation in China is higher than that worldwide (0.92% higher per day). Higher population aggregation and urbanization directly affect the epidemic, and weather is an indirect factor. [ABSTRACT FROM AUTHOR]

During epidemics people may reduce their social and economic activity to lower their risk of infection. Such social distancing strategies will depend on information about the course of the epidemic but also on when they expect the epidemic to end, for instance due to vaccination. Typically it is difficult to make optimal decisions, because the available information is incomplete and uncertain. Here, we show how optimal decision-making depends on information about vaccination timing in a differential game in which individual decision-making gives rise to Nash equilibria, and the arrival of the vaccine is described by a probability distribution. We predict stronger social distancing the earlier the vaccination is expected and also the more sharply peaked its probability distribution. In particular, equilibrium social distancing only meaningfully deviates from the no-vaccination equilibrium course if the vaccine is expected to arrive before the epidemic would have run its course. We demonstrate how the probability distribution of the vaccination time acts as a generalised form of discounting, with the special case of an exponential vaccination time distribution directly corresponding to regular exponential discounting. [ABSTRACT FROM AUTHOR]

We present a novel approach to estimate the time-varying ascertainment rate in almost real-time, based on the surveillance of positively tested infectious and hospital admission data. We also address the age dependence of the estimate. The ascertainment rate estimation is based on the Bayes theorem. It can be easily calculated and used (i) as part of a mechanistic model of the disease spread or (ii) to estimate the unreported infections or changes in their proportion in almost real-time as one of the early-warning signals in case of undetected outbreak emergence. The paper also contains a case study of the COVID-19 epidemic in the Czech Republic. The case study demonstrates the usage of the ascertainment rate estimate in retrospective analysis, epidemic monitoring, explanations of differences between waves, usage in the national Anti-epidemic system, and monitoring of the effectiveness of non-pharmaceutical interventions on Czech nationwide surveillance datasets. The Czech data reveal that the probability of hospitalization due to SARS-CoV-2 infection for the senior population was 12 times higher than for the non-senior population in the monitored period from the beginning of March 2020 to the end of May 2021. In a mechanistic model of COVID-19 spread in the Czech Republic, the ascertainment rate enables us to explain the links between all basic compartments, including new cases, hospitalizations, and deaths. [ABSTRACT FROM AUTHOR]

An infectious disease spreads not only over a single population or community but also across multiple and heterogeneous communities. Moreover, its transmissibility varies over time because of various factors such as seasonality and epidemic control, which results in strongly nonstationary behavior. In conventional methods for assessing transmissibility trends or changes, univariate time-varying reproduction numbers are calculated without taking into account transmission across multiple communities. In this paper, we propose a multivariate-count time series model for epidemics. We also propose a statistical method for estimating the transmission of infections across multiple communities and the time-varying reproduction numbers of each community simultaneously from a multivariate time series of case counts. We apply our method to incidence data for the novel coronavirus disease 2019 (COVID-19) pandemic to reveal the spatiotemporal heterogeneity of the epidemic process. [ABSTRACT FROM AUTHOR]

Based on the extensive data accumulated during the COVID-19 pandemic, we put forward simple to implement indicators, that should alert authorities and provide early warnings of an impending sanitary crisis. In fact, Testing, Tracing, and Isolation (TTI) in conjunction with disciplined social distancing and vaccination were expected to achieve negligible COVID-19 contagion levels; however, they proved to be insufficient, and their implementation has led to controversial social, economic and ethical challenges. This paper focuses on the development of simple indicators, based on the experience gained by COVID-19 data, which provide a sort of yellow light as to when an epidemic might expand, despite some short term decrements. We show that if case growth is not stopped during the 7 to 14 days after onset, the growth risk increases considerably, and warrants immediate attention. Our model examines not only the COVID contagion propagation speed, but also how it accelerates as a function of time. We identify trends that emerge under the various policies that were applied, as well as their differences among countries. The data for all countries was obtained from ourworldindata.org. Our main conclusion is that if the reduction spread is lost during one, or at most two weeks, urgent measures should be implemented to avoid scenarios in which the epidemic gains strong impetus. [ABSTRACT FROM AUTHOR]

The Susceptible-Exposed-Infectious-Recovered (SEIR) epidemic model has been commonly used to analyze the spread of infectious diseases. This 4-compartment (S, E, I and R) model uses an approximation of temporal homogeneity of individuals in these compartments to calculate the transfer rates of the individuals from compartment E to I to R. Although this SEIR model has been generally adopted, the calculation errors caused by temporal homogeneity approximation have not been quantitatively examined. In this study, a 4-compartment l-i SEIR model considering temporal heterogeneity was developed from a previous epidemic model (Liu X., Results Phys. 2021; 20:103712), and a closed-form solution of the l-i SEIR model was derived. Here, l represents the latent period and i represents the infectious period. Comparing l-i SEIR model with the conventional SEIR model, we are able to examine how individuals move through each corresponding compartment in the two SEIR models to find what information may be missed by the conventional SEIR model and what calculation errors may be introduced by using the temporal homogeneity approximation. Simulations showed that l-i SEIR model could generate propagated curves of infectious cases under the condition of l>i. Similar propagated epidemic curves were reported in literature, but the conventional SEIR model could not generate propagated curves under the same conditions. The theoretical analysis showed that the conventional SEIR model overestimates or underestimates the rate at which individuals move from compartment E to I to R in the rising or falling phase of the number of infectious individuals, respectively. Increasing the rate of change in the number of infectious individuals leads to larger calculation errors in the conventional SEIR model. Simulations from the two SEIR models with assumed parameters or with reported daily COVID-19 cases in the United States and in New York further confirmed the conclusions of the theoretical analysis. [ABSTRACT FROM AUTHOR]

Epidemics, such as COVID-19, have caused significant harm to human society worldwide. A better understanding of epidemic transmission dynamics can contribute to more efficient prevention and control measures. Compartmental models, which assume homogeneous mixing of the population, have been widely used in the study of epidemic transmission dynamics, while agent-based models rely on a network definition for individuals. In this study, we developed a real-scale contact-dependent dynamic (CDD) model and combined it with the traditional susceptible-exposed-infectious-recovered (SEIR) compartment model. By considering individual random movement and disease spread, our simulations using the CDD-SEIR model reveal that the distribution of agent types in the community exhibits spatial heterogeneity. The estimated basic reproduction number R0 depends on group mobility, increasing logarithmically in strongly heterogeneous cases and saturating in weakly heterogeneous conditions. Notably, R0 is approximately independent of virus virulence when group mobility is low. We also show that transmission through small amounts of long-term contact is possible due to short-term contact patterns. The dependence of R0 on environment and individual movement patterns implies that reduced contact time and vaccination policies can significantly reduce the virus transmission capacity in situations where the virus is highly transmissible (i.e., R0 is relatively large). This work provides new insights into how individual movement patterns affect virus spreading and how to protect people more efficiently. [ABSTRACT FROM AUTHOR]

An epidemic not attributable to plague caused thousands of deaths in Milan in the summer of 1629, a time of war and famine that immediately preceded the even more fatal Great Plague of 1630 that killed an estimated ten of thousands of people. The 5,993 deaths of 1629 recorded in the Liber Mortuorum of Milan (a city with an estimated population of 130,000 inhabitants at the time) were 45.7% more than the average number recorded between 1601 and 1628. Registered deaths peaked in July, and 3,363 of the deaths (56,1%) were attributed to a febrile illness which, in most cases (2,964, 88%), was not associated with a rash or organ involvement. These deaths involved 1,627 males and 1,334 females and occurred at a median age of 40 years (range 0–95). In this paper, we discuss the possible cause of the epidemic, which may have been an outbreak of typhoid fever. [ABSTRACT FROM AUTHOR]

Background: Miami, Florida is an epicenter of the HIV epidemic in the US, with 20% of new HIV infections occurring in women. Despite effectiveness of Pre-Exposure Prophylaxis (PrEP) in preventing HIV, only 10% of eligible women benefit from its use. Setting: This study evaluates PrEP awareness and use, and factors associated with PrEP awareness among sexually active women in Miami, Florida. Methods: Results reported in this study included cross-sectional data that were collected as part of a baseline visit from a parent study. Cis-gender, HIV-negative, 18-45-year-old, sexually active women were recruited as part of a study evaluating recurrent bacterial vaginosis and HIV risk. Participants completed questionnaires assessing socio-demographics, HIV risk factors, prior history of HIV testing and reproductive tract infections, PrEP awareness and use. Relationships between variables and PrEP awareness were analyzed and multivariable logistic regression identified variables strongly associated with PrEP awareness. Results: Among the 295 women enrolled, median age was 31 (24–38) years, 49% Black, 39% White, and 34% Hispanic. Of 63% who knew about PrEP, only 5% were on PrEP. Women with income below poverty line (OR = 2.00[1.04,3.87];p = 0.04), more male sexual partners in past month (OR = 1.30[1.01,1.68];p = 0.04), lifetime HIV testing (OR = 6.42[2.83,14.52];p<0.01), and current bacterial vaginosis (OR = 2.28[1.18,4.40];p = 0.01) were more likely to be aware of PrEP. Lower odds of PrEP awareness were associated with being Black (OR = 0.38[0.15,0.96];p = 0.04), Hispanic (OR = 0.18[0.08,0.39];p<0.01), heterosexual (OR = 0.29[0.11,0.77];p<0.01), and reporting inconsistent condom use during vaginal sex (OR = 0.21[0.08,0.56];p<0.01). Conclusion: PrEP awareness is low among reproductive age women in a high-risk setting. Culturally tailored interventions are needed to increase PrEP awareness and uptake, especially among Black and Hispanic women with inconsistent condom use during vaginal sex with male partners. [ABSTRACT FROM AUTHOR]

The mathematical interpretation of interventions for the mitigation of epidemics in the literature often involves finding the optimal time to initiate an intervention and/or the use of the number of infections to manage impact. Whilst these methods may work in theory, in order to implement effectively they may require information which is not likely to be available in the midst of an epidemic, or they may require impeccable data about infection levels in the community. In reality, testing and cases data can only be as good as the policy of implementation and the compliance of the individuals, which implies that accurately estimating the levels of infections becomes difficult or complicated from the data that is provided. In this paper, we demonstrate a different approach to the mathematical modelling of interventions, not based on optimality or cases, but based on demand and capacity of hospitals who have to deal with the epidemic on a day to day basis. In particular, we use data-driven modelling to calibrate a susceptible-exposed-infectious-recovered-died type model to infer parameters that depict the dynamics of the epidemic in several regions of the UK. We use the calibrated parameters for forecasting scenarios and understand, given a maximum capacity of hospital healthcare services, how the timing of interventions, severity of interventions, and conditions for the releasing of interventions affect the overall epidemic-picture. We provide an optimisation method to capture when, in terms of healthcare demand, an intervention should be put into place given a maximum capacity on the service. By using an equivalent agent-based approach, we demonstrate uncertainty quantification on the likelihood that capacity is not breached, by how much if it does, and the limit on demand that almost guarantees capacity is not breached. [ABSTRACT FROM AUTHOR]

Since 2003, the California West Nile virus (WNV) dead bird surveillance program (DBSP) has monitored publicly reported dead birds for WNV surveillance and response. In the current paper, we compared DBSP data from early epidemic years (2004–2006) with recent endemic years (2018–2020), with a focus on specimen collection criteria, county report incidence, bird species selection, WNV prevalence in dead birds, and utility of the DBSP as an early environmental indicator of WNV. Although fewer agencies collected dead birds in recent years, most vector control agencies with consistent WNV activity continued to use dead birds as a surveillance tool, with streamlined operations enhancing efficiency. The number of dead bird reports was approximately ten times greater during 2004–2006 compared to 2018–2020, with reports from the Central Valley and portions of Southern California decreasing substantially in recent years; reports from the San Francisco Bay Area decreased less dramatically. Seven of ten counties with high numbers of dead bird reports were also high human WNV case burden areas. Dead corvid, sparrow, and quail reports decreased the most compared to other bird species reports. West Nile virus positive dead birds were the most frequent first indicators of WNV activity by county in 2004–2006, followed by positive mosquitoes; in contrast, during 2018–2020 mosquitoes were the most frequent first indicators followed by dead birds, and initial environmental WNV detections occurred later in the season during 2018–2020. Evidence for WNV impacts on avian populations and susceptibility are discussed. Although patterns of dead bird reports and WNV prevalence in tested dead birds have changed, dead birds have endured as a useful element within our multi-faceted WNV surveillance program. [ABSTRACT FROM AUTHOR]

With countries across the world facing repeated epidemic waves, it becomes critical to monitor, mitigate and prevent subsequent waves. Common indicators like active case numbers may not be sensitive enough in the presence of systemic inefficiencies like insufficient testing or contact tracing. Test positivity rates are sensitive to testing strategies and cannot estimate the extent of undetected cases. Reproductive numbers estimated from logarithms of new incidences are inaccurate in dynamic scenarios and not sensitive enough to capture changes in efficiencies. Systemic fatigue results in lower testing, inefficient tracing and quarantining thereby precipitating the onset of the epidemic wave. We propose a novel indicator for detecting the slippage of test-trace efficiency based on the number of deaths/hospitalizations resulting from known and hitherto unknown infections. This can also be used to forecast an epidemic wave that is advanced or exacerbated due to a drop in efficiency in situations where the testing has come down drastically and contact tracing is virtually nil as is prevalent currently. Using a modified SEIRD epidemic simulator we show that (i) Ratio of deaths/hospitalizations from an undetected infection to total deaths converges to a measure of systemic test-trace inefficiency. (ii) This index forecasts the slippage in efficiency earlier than other known metrics. (iii) Mitigation triggered by this index helps reduce peak active caseload and eventual deaths. Deaths/hospitalizations accurately track the systemic inefficiencies and detect latent cases. Based on these results we make a strong case that administrations use this metric in the ensemble of indicators. Further, hospitals may need to be mandated to distinctly register deaths/hospitalizations due to previously undetected infections. Thus the proposed metric is an ideal indicator of an epidemic wave that poses the least socio-economic cost while keeping the surveillance robust during periods of pandemic fatigue. [ABSTRACT FROM AUTHOR]

Global mitigation strategies to tackle the threat posed by SARS-CoV-2 have produced a significant decrease of the severity of 2020/21 seasonal influenza, which might result in a reduced population natural immunity for the upcoming 2021/22 influenza season. To predict the spread of influenza virus in Italy and the impact of prevention and control measures, we present an age-structured Susceptible-Exposed-Infectious-Removed (SEIR) model including the role of social mixing patterns and the impact of age-stratified vaccination strategies and Non-Pharmaceutical Interventions (NPIs) such as school closures, partial lockdown, as well as the adoption of personal protective equipment and the practice of hand hygiene. We find that vaccination campaigns with standard coverage would produce a remarkable mitigation of the spread of the disease in moderate influenza seasons, making the adoption of NPIs unnecessary. However, in case of severe seasonal epidemics, a standard vaccination coverage would not be sufficiently effective in fighting the epidemic, thus implying that a combination with the adoption of NPIs is necessary to contain the disease. Alternatively, our results show that the enhancement of the vaccination coverage would reduce the need to adopt NPIs, thus limiting the economic and social impacts that NPIs might produce. Our results highlight the need to respond to the influenza epidemic by strengthening the vaccination coverage. [ABSTRACT FROM AUTHOR]

Why was there considerable variation in initial COVID-19 mortality impact across countries? Through a configurational lens, this paper examines which configurations of five conditions—a delayed public-health response, past epidemic experience, proportion of elderly in population, population density, and national income per capita—influence early COVID-19 mortality impact measured by years of life lost (YLL). A fuzzy-set qualitative comparative analysis (fsQCA) of 80 countries identifies four distinctive pathways associated with high YLL rate and four other different pathways leading to low YLL rate. Results suggest that there is no singular "playbook"—a set of policies that countries can follow. Some countries failed differently, whereas others succeeded differently. Countries should take into account their situational contexts to adopt a holistic response strategy to combat any future public-health crisis. Regardless of the country's past epidemic experience and national income levels, a speedy public-health response always works well. For high-income countries with high population density or past epidemic experience, they need to take extra care to protect elderly populations who may otherwise overstretch healthcare capacity. [ABSTRACT FROM AUTHOR]

Understanding the spread of COVID-19 has been the subject of numerous studies, highlighting the significance of reliable epidemic models. Here, we introduce a novel epidemic model using a latent Hawkes process with temporal covariates for modelling the infections. Unlike other models, we model the reported cases via a probability distribution driven by the underlying Hawkes process. Modelling the infections via a Hawkes process allows us to estimate by whom an infected individual was infected. We propose a Kernel Density Particle Filter (KDPF) for inference of both latent cases and reproduction number and for predicting the new cases in the near future. The computational effort is proportional to the number of infections making it possible to use particle filter type algorithms, such as the KDPF. We demonstrate the performance of the proposed algorithm on synthetic data sets and COVID-19 reported cases in various local authorities in the UK, and benchmark our model to alternative approaches. [ABSTRACT FROM AUTHOR]

BackgroundTwo manufacturers, Maxim Biomedical and Sedia Biosciences Corporation, supply CDC-approved versions of the HIV-1 Limiting Antigen Avidity EIA (LAg) for detecting 'recent' HIV infection in cross-sectional incidence estimation. This study assesses and compares the performance of the two assays for incidence surveillance.MethodsWe ran both assays on a panel of 2,500 well-characterized HIV-1-infected specimens. We analysed concordance of assay results, assessed reproducibility using repeat testing and estimated mean durations of recent infection (MDRIs) and false-recent rates (FRRs) for a range of normalized optical density (ODn) thresholds, alone and in combination with viral load thresholds. We defined three hypothetical surveillance scenarios, similar to the Kenyan and South African epidemics, and a concentrated epidemic. These scenarios allowed us to evaluate the precision of incidence estimates obtained by means of various recent infection testing algorithms (RITAs) based on each of the two assays.ResultsThe Maxim assay produced lower ODn values than the Sedia assay on average, largely as a result of higher calibrator readings (mean OD of 0.749 vs. 0.643), with correlation of normalized readings lower (R2 = 0.908 vs. R2 = 0.938). Reproducibility on blinded control specimens was slightly better for Maxim. The MDRI of a Maxim-based algorithm at the 'standard' threshold (ODn ≤1.5 & VL >1,000) was 201 days (95% CI: 180,223) and for Sedia 171 (152,191). The difference Differences in MDRI were estimated at 32.7 (22.9,42.8) and 30.9 days (21.7,40.7) for the two algorithms, respectively. Commensurately, the Maxim algorithm had a higher FRR in treatment-naive subjects (1.7% vs. 1.1%). The two assays produced similar precision of incidence estimates in the three surveillance scenarios.ConclusionsDifferences between the assays can be primarily attributed to the calibrators supplied by the manufacturers. Performance for surveillance was extremely similar, although different thresholds were optimal (i.e. produced the lowest variance of incidence estimates) and at any given ODn threshold, different estimates of MDRI and FRR were obtained. The two assays cannot be treated as interchangeable: assay and algorithm-specific performance characteristic estimates must be used for survey planning and incidence estimation.

RationaleAs part of the End TB Strategy, the World Health Organization calls for low-tuberculosis (TB) incidence settings to achieve pre-elimination (

As the COVID-19 pandemic fades, the aviation industry is entering a fast recovery period. To analyze airport networks' post-pandemic resilience during the recovery process, this paper proposes a Comprehensive Resilience Assessment (CRA) model approach using the airport networks of China, Europe, and the U.S.A as case studies. The impact of COVID-19 on the networks is analyzed after populating the models of these networks with real air traffic data. The results suggest that the pandemic has caused damage to all three networks, although the damages to the network structures of Europe and the U.S.A are more severe than the damage in China. The analysis suggests that China, as the airport network with less network performance change, has a more stable level of resilience. The analysis also shows that the different levels of stringency policy in prevention and control measures during the epidemic directly affected the recovery rate of the network. This paper provides new insights into the impact of the pandemic on airport network resilience. [ABSTRACT FROM AUTHOR]

Sarcoptic mange poses a serious conservation threat to endangered San Joaquin kit foxes (Vulpes macrotis mutica). After first appearing in Bakersfield, California in spring 2013, mange reduced the kit fox population approximately 50% until the epidemic ended with minimally detectable endemic cases after 2020. Mange is lethal and thus, with such a high force of infection and lack of immunity, it remains unclear why the epidemic did not burn itself out rapidly and how it persisted so long. Here we explored spatio-temporal patterns of the epidemic, analyzed historical movement data, and created a compartment metapopulation model (named "metaseir") to evaluate whether movement of foxes among patches and spatial heterogeneity would reproduce the eight years epidemic with 50% population reduction observed in Bakersfield. Our main findings from metaseir were that: 1) a simple metapopulation model can capture the Bakersfield-like disease epidemic dynamics even when there is no environmental reservoir or external spillover host, 2) the most impactful parameter on persistence and magnitude of the epidemic is the projection, β/αβ (transmission over decay rate of transmission over space), 3) heterogeneity in patch carrying capacities changes the critical value of the projection needed to achieve an epidemic but makes little difference to epidemic persistence time, and 4) the epidemic is relatively insensitive to birth rates and density vs. frequency-dependent transmission. Our model can help guide management and assessment of metapopulation viability of this vulpid subspecies, while the exploratory data analysis and model will also be valuable to understand mange in other, particularly den-occupying, species. [ABSTRACT FROM AUTHOR]

Epidemics and pandemics require an early estimate of the cumulative infection prevalence, sometimes referred to as the infection "Iceberg," whose tip are the known cases. Accurate early estimates support better disease monitoring, more accurate estimation of infection fatality rate, and an assessment of the risks from asymptomatic individuals. We find the Pivot group, the population sub-group with the highest probability of being detected and confirmed as positively infected. We differentiate infection susceptibility, assumed to be almost uniform across all population sub-groups at this early stage, from the probability of being confirmed positive. The latter is often related to the likelihood of developing symptoms and complications, which differs between sub-groups (e.g., by age, in the case of the COVID-19 pandemic). A key assumption in our method is the almost-random subgroup infection assumption: The risk of initial infection is either almost uniform across all population sub-groups or not higher in the Pivot sub-group. We then present an algorithm that, using the lift value of the pivot sub-group, finds a lower bound for the cumulative infection prevalence in the population, that is, gives a lower bound on the size of the entire infection "Iceberg." We demonstrate our method by applying it to the case of the COVID-19 pandemic. We use UK and Spain serological surveys of COVID-19 in its first year to demonstrate that the data are consistent with our key assumption, at least for the chosen pivot sub-group. Overall, we applied our methods to nine countries or large regions whose data, mainly during the early COVID-19 pandemic phase, were available: Spain, the UK at two different time points, New York State, New York City, Italy, Norway, Sweden, Belgium, and Israel. We established an estimate of the lower bound of the cumulative infection prevalence for each of them. We have also computed the corresponding upper bounds on the infection fatality rates in each country or region. Using our methodology, we have demonstrated that estimating a lower bound for an epidemic's infection prevalence at its early phase is feasible and that the assumptions underlying that estimate are valid. Our methodology is especially helpful when serological data are not yet available to gain an initial assessment on the prevalence scale, and more so for pandemics with an asymptomatic transmission, as is the case with Covid-19. [ABSTRACT FROM AUTHOR]

We first qualitatively divide the cycle of an infectious disease outbreak into five distinct stages by following the adoption categorization from the diffusion theory. Next, we apply a standard mechanistic model, the susceptible-infected-recovered model, to simulate a variety of transmission scenarios and to quantify the benefits of various countermeasures. In particular, we apply the specific values of the newly infected to quantitatively divide an outbreak cycle into stages. We therefore reveal diverging patterns of countermeasures in different stages. The stage is critical in determining the evolutionary characteristics of the diffusion process. Our results show that it is necessary to employ appropriate diverse strategies in different stages over the life cycle of an infectious disease outbreak. In the early stages, we need to focus on prevention, early detection, and strict countermeasure (e.g., isolation and lockdown) for controlling an epidemic. It is better safe (i.e., stricter countermeasures) than sorry (i.e., let the virus spread out). There are two reasons why we should implement responsive and strict countermeasures in the early stages. The countermeasures are very effective, and the earlier the more total infected reduction over the whole cycle. The economic and societal burden for implementing countermeasures is relatively small due to limited affected areas, and the earlier the less burden. Both reasons change to the opposite in the late stages. The strategic focuses in the late stages become more delicate and balanced for two reasons: the same countermeasures become much less effective, and the society bears a much heavier burden. Strict countermeasures may become unnecessary, and we need to think about how to live with the infectious disease. [ABSTRACT FROM AUTHOR]

Background: After COVID-19 vaccines received approval, vaccination campaigns were launched worldwide. Initially, these were characterized by a shortage of vaccine supply, and specific risk groups were prioritized. Once supply was guaranteed and vaccination coverage saturated, the focus shifted from risk groups to anti-vaxxers, the under-aged population, and regions of low coverage. At the same time, hopes to reach herd immunity by vaccination campaigns were put into perspective by the emergence and spread of more contagious and aggressive viral variants. Particularly, concerns were raised that not all vaccines protect against the new-emerging variants. The objective of this study is to introduce a predictive model to quantify the effect of vaccination campaigns on the spread of SARS-CoV-2 viral variants. Methods and findings: The predictive model introduced here is a comprehensive extension of the one underlying the pandemic preparedness tool CovidSim 2.0 (http://covidsim.eu/). The model is age and spatially stratified, incorporates a finite (but arbitrary) number of different viral variants, and incorporates different vaccine products. The vaccines are allowed to differ in their vaccination schedule, vaccination rates, the onset of vaccination campaigns, and their effectiveness. These factors are also age and/or location dependent. Moreover, the effectiveness and the immunizing effect of vaccines are assumed to depend on the interaction of a given vaccine and viral variant. Importantly, vaccines are not assumed to immunize perfectly. Individuals can be immunized completely, only partially, or fail to be immunized against one or many viral variants. Not all individuals in the population are vaccinable. The model is formulated as a high-dimensional system of differential equations, which is implemented efficiently in the programming language Julia. As an example, the model was parameterized to reflect the epidemic situation in Germany until November 2021 and future dynamics of the epidemic under different interventions were predicted. In particular, without tightening contact reductions, a strong epidemic wave is predicted during December 2021 and January 2022. Provided the dynamics of the epidemic in Germany, in late 2021 administration of full-dose vaccination to all eligible individuals (e.g. by mandatory vaccination) would be too late to have a strong effect on reducing the number of infections in the fourth wave in Germany. However, it would reduce mortality. An emergency brake, i.e., an incidence-based stepwise lockdown, would be efficient to reduce the number of infections and mortality. Furthermore, to specifically account for mobility between regions, the model was applied to two German provinces of particular interest: Saxony, which currently has the lowest vaccine rollout in Germany and high incidence, and Schleswig-Holstein, which has high vaccine rollout and low incidence. Conclusions: A highly sophisticated and flexible but easy-to-parameterize model for the ongoing COVID-19 pandemic is introduced. The model is capable of providing useful predictions for the COVID-19 pandemic, and hence provides a relevant tool for epidemic decision-making. The model can be adjusted to any country, and the predictions can be used to derive the demand for hospital or ICU capacities. [ABSTRACT FROM AUTHOR]

Random walks on graphs are often used to analyse and predict epidemic spreads and to investigate possible control actions to mitigate them. In this study, we first show that models based on random walks with a single stochastic agent (such as Google's popular PageRank) may provide a poor description of certain features of epidemic spread: most notably, spreading times. Then, we discuss another Markov chain based method that does reflect the correct mean infection times for the disease to spread between individuals in a network, and we determine a procedure that allows one to compute them efficiently via a sampling strategy. Finally, we present a novel centrality measure based on infection times, and we compare its node ranking properties with other centrality measures based on random walks. Our results are provided for a simple SI model for epidemic spreading. [ABSTRACT FROM AUTHOR]

This study examines the lessons learnt from the implementation of a surge program in Akwa Ibom State, Nigeria as part of the Strengthening Integrated Delivery of HIV/AIDS Services (SIDHAS) Project. In this analysis, we included all clients who received HIV counseling and testing services, tested HIV positive, and initiated ART in SIDHAS-supported local government areas (LGAs) from April 2017 to March 2021. We employed descriptive and inferential statistics to analyze our results. A total of 2,018,082 persons were tested for HIV. Out of those tested, 102,165 (5.1%) tested HIV-positive. Comparing the pre-surge and post-surge periods, we observed an increase in HIV testing from 490,450 to 2,018,082 (p≤0.031) and in HIV-positive individuals identified from 21,234 to 102,165 (p≤0.001) respectively. Of those newly identified positives during the surge, 98.26% (100,393/102,165) were linked to antiretroviral therapy compared to 99.24% (21,073/21,234) pre-surge. Retention improved from 83.3% to 92.3% (p<0.001), and viral suppression improved from 73.5% to 96.2% (p<0.001). A combination of community and facility-based interventions implemented during the surge was associated with the rapid increase in case finding, retention, and viral suppression; propelling the State towards HIV epidemic control. HIV programs should consider a combination of community and facility-based interventions in their programming. [ABSTRACT FROM AUTHOR]