Level Of Infection Risk Research Articles

Effects of recirculation and air change per hour on COVID-19 transmission in indoor settings: A CFD study with varying HVAC parameters

COVID-19 has already claimed over 7 million lives and has infected over 775 million people globally [1]. SARS-CoV-2, the virus that causes Covid-19, spreads primarily through droplets from infected people's airways, rendering Heating, Ventilation, and Air Conditioning (HVAC) systems critical in controlling infection risk levels in the indoor environment. To understand the dynamics of exhaled droplets and aerosols and the percentage of particles that are inhaled, escaped, recirculated, or trapped on different surfaces for a variety of environmental settings, we have presented our findings from the Computational Fluid Dynamics (CFD) modeling to investigate the impact of changing HVAC parameters in this paper. When combined with the spatial and temporal distribution of droplets, this method can be used to assess the potential risk and strengthen resilience. This finding demonstrates the viability and usefulness of CFD for modeling the distribution and dynamics of droplets and aerosols in confined environments. Our study demonstrates that raising the Air Change per Hour (ACH) from 2 to 8 reduces the risk of particle inhalation by nearly 70 %. Additionally, limiting the amount of air recirculation or increasing the amount of fresh air helps to reduce the number of airborne particles in an indoor space. To reduce the potential for respiratory droplet-related transmission and to provide relevant recommendations to the appropriate authority, the same computational approach could be applied to a wide range of ventilated indoor environments such as public buses, restaurants, exhibitions, and theaters.

Restricted health service utilization and subsequent positive self-care behavior during the early COVID-19 pandemic in China.

The reallocation of health resources, epidemic prevention and control measures during the COVID-19 pandemic triggered widespread restricted health service utilization, some residents and patients tried positive self-care behavior to maintain their health, yet the efficacy of this intervention remains unclear. Based on the reasoned action approach (RAA) theory, this study aimed to investigate the correlation between self-care behavior and restricted health service utilization among adults in China, trying to discover the vulnerable groups and external and intrinsic factors that affect self-care behavior among Chinese adults. Data on demographics, socioeconomic, health status, and self-care behavior were collected in "The Early China COVID-19 Survey," a cross-sectional anonymous online survey of the general population in China. Self-care behavior was measured by four indicators: weight control (WC), physical activity (PA), prevention behavior (PB), and online medical consultation (OMC). The multiple linear models and binary logistic regression were used to examine whether restricted health service utilization (RHSU) is associated with self-care behaviors; also, adjusted multivariate logistic regression was used to analyze subgroup heterogeneity. In total, 8,428 adult participants completed the survey, the mean OMC score was 1.51 (SD 1.34), the mean PB score was 18.17 (SD 3.44), and the proportion of participants who engaged in WC and PA was 42.30 and 62.57%, respectively. According to the multiple regression model, the RHSU was significantly positively correlated with all four indicators of self-care (WC: OR = 1.34, p < 0.001, PA: OR = 1.34, p < 0.05, MC: OR = 1.30, p < 0.001, PB: coef = 0.16, p < 0.05). We also observed some significant differences in the intensity of this relationship by subgroup analysis, precisely, OMC (high vs. moderate vs. low infection-risk level: OR = 1.48; 1.41; 1.19, p < 0.1), PA (male vs. female: OR = 1.27;1.06; p < 0.05, high vs. Moderate and low infection-risk level: OR = 1.51; 1.17; 1.02, p < 0.05), PB (Chronic disease groups vs. no: coef = 0.46; 0.1, p < 0.05). Restricted health service utilization predicts more positive self-care behavior, and the intensity of partial correlation was significantly different in the subgroups of sex, actual infection risk level of the living area, and chronic diseases. These findings highlight the urgent demand for self-care behavior among Chinese adults during the pandemic and provide new insights for developing self-care and reducing the burden on the healthcare system in the long term.

The Relationship Between Attitude, Belief, Experience, and Knowledge of Iranian Nurses Toward the Use of Personal Protective Equipment: A Cross-Sectional Study.

Utilizing Personal Protective Equipment (PPE) is pivotal in averting infection transmission to both patients and nurses. The attitude of nurses is a critical determinant in their compliance with PPE usage. This study seeks to explore the correlation between the attitudes of Iranian nurses and their beliefs, experiences, and knowledge concerning the application of PPE. In this cross-sectional study, 303 nurses employed in hospitals affiliated with Kurdistan University of Medical Sciences (Iran) were chosen using a quota sampling technique. Between April and June 2022, they completed self-administered questionnaires, which consisted of a Demographic Information form and a four-part questionnaire on "Attitude, Belief, Experience, Knowledge" concerning the utilization of PPE. Data were analyzed utilizing descriptive and inferential statistical methods. The multiple linear regression model was applied to investigate the relationship between attitude scores and various examined variables. The findings indicated that most participants held bachelor's degrees (93.07%), and their attitude scores toward using PPE exceeded 3.25 out of 6 for all 12 questions. Female gender, increased work experience, and higher organizational positions exhibited positive and significant associations with a favorable attitude toward PPE utilization. Conversely, the absence of training related to PPE, a lack of belief in infection control, and limited knowledge displayed negative correlations. Nurses have an ethical obligation to adhere to infection control guidelines, including consistently utilizing PPE, regardless of the level of infection risk or the visibility of the infection. Continuous training and regular monitoring of nurses in this context are indispensable.

Insights into the risk of COVID-19 infection in urban neighborhood and its environmental influence factors:A novel approach

Many studies have dealt with the relationship between the built environment and COVID-19 infection in isolation. However, the research approaches were often inadequate in accurately reflecting the risk of COVID-19 infection. Therefore, we aim to develop a novel approach to study the risk of COVID-19 infection and its Environmental Influence Factors (EIFs) at the neighborhood scale. We constructed a multi-agent model of the spatial environment of residential quarters, including models of behavior and COVID-19 infection dynamics, and established 24 experimental schemes based on the Prevention and Control Measures (PCMs) of zonal control, mask-wearing, and outing frequency to simulate and assess the infection risk. The results show that the areas of accessible wood, hard surfaces, and seating are high-risk sites for infection. Zonal control and mask-wearing are effective PCMs, and the EIFs we set all play significant roles in influencing infection risk. Our study provides insight into the effects of PCMs and EIFs related to COVID-19 infection and highlights that the novel approach not only reveals the levels of infection risk and its mechanisms but also detects the spatio-temporal characteristics of behaviors and infection. This novel approach, dealing with individuals, space, environment, behavior, PCMs, and COVID-19 infection dynamics in integration, overcomes the limitations of traditional methods that overlook the influences of the spatio-temporal dynamics of individual behaviors and their environmental factors. It offers theoretical support for designing healthy outdoor built environments and technical support for the PCMs of the epidemic.

Superspreading of SARS-CoV-2 at a choir rehearsal in Finland-A computational fluid dynamics view on aerosol transmission and patient interviews.

COVID-19 pandemic has highlighted the role of aerosol transmission and the importance of superspreading events. We analyzed a choir rehearsal in November 2020, where all participants, except one who had recently earlier recovered from COVID-19, were infected. We explore the risk factors for severe disease in this event and model the aerosol dispersion in the rehearsal room. Characteristics of participants were collected by interviews and supplemented with patient records. A computational simulation of aerosol distribution in the rehearsal room and the efficacy of potential safety measures was conducted using the Large-Eddy Simulation approach. Infection risk was studied by analyzing quanta emission and exposure with the Wells-Riley equation. The simulation showed that airborne transmission likely explains this mass contagion event. Every singer was exposed to the virus in only 5 min from the beginning of the rehearsal, and maximum concentration levels were reached at 20 min the concentration levels started to approach a steady state after 20 min. Although concentration differences existed in the room, risk levels near (1 m) and far (5 m) from the aerosol source were similar for certain singers. Modeling indicated infection risk levels of 70-100% after one hour; the risk would have been considerably reduced by wearing high-filtration respirators. Age and pre-existing comorbidities predicted more severe disease. The high incidence of illness may be partly attributed to the relatively high median age of individuals. Additionally, those admitted to the hospital had multiple underlying health conditions that predispose them to more severe disease. Airborne transmission and indoor space can explain this mass exposure event. High-filtration respirators could have prevented some infections. The importance of safety distances diminishes the longer the indoor event. The concept of safety distance is challenging, as our study suggests that long range airborne transmission may occur in indoor events with extended duration. We encourage informing the public, especially persons at risk, of safety measures during epidemics.

Research on demand forecasting and distribution of emergency medical supplies using an agent-based model

The global health crisis caused by SARS-CoV-2 since 2019 has emphasized the critical significance of effective disease detection and treatment in minimizing infection rates and fatalities, as well as halting the spread of pandemics. During an outbreak, individuals suspected of being infected require a significant amount of testing resources, while those confirmed to be infected demand substantial treatment resources. Hence, this paper is dedicated to presenting a new pandemic model that enables joint forecasting and allocation of resources for testing and treatment. The proposed model in this paper is an innovative agent-based epidemic compartmental model, which also incorporates a mixed integer model. It integrates novel features based on crucial disease characteristics, such as self-healing for asymptomatic or mild-symptomatic cases, varying infection risk levels among different groups, and the inclusion of secondary infections. Moreover, the solutions of the joint allocation model are compared with those of the independent allocation model, which entails considering resource interactions rather than allocating each resource independently. Furthermore, the validity of this model was confirmed through real-world data obtained during the SARS-CoV-2 outbreak in China. The findings offer valuable insights into the impact of intervention levels and duration, joint allocation schemes, as well as optimal allocation of test and treatment resources on cross-regional transmission of the pandemic.

Effects of Circuit Exercise Training Intervention on Health- Related Physical Fitness and Biomarkers for the Elderly People with Cardiovascular Diseases

This study aimed to investigate the effects of a 12-week circuit exercise intervention on the health-related physical fitness and biomarkers of elderly with cardiovascular diseases. Sixty-seven subjects participated in the study, included stretching, aerobic, and resistance training. Pre- and post-examination blood testing and fitness testing. Paired samples t-tests, and correlation analysis were used to analyse the data. Results showed that subjects were overweight and had medium risk levels of infection for cardiovascular diseases. Cardiovascular and strength performance improved, but sit-up performance was lower than the norm. Total cholesterol and systolic blood pressure improved, but systolic blood pressure remained at a pre-hypertension level. The intervention had a significant positive effect on health-related physical fitness and biomarkers with medium risk cluster of cardiovascular diseases, and there was a high correlation between the two. BMI had a positive correlation with biomarker index and cardiovascular fitness, while agility andflexibility had a negative correlation.

Real-time risk ranking of emerging epidemics based on optimized moving average prediction limit—taking the COVID-19 pandemic as an example

BackgroundMathematical models to forecast the risk trend of the COVID-19 pandemic timely are of great significance to control the pandemic, but the requirement of manual operation and many parameters hinders their efficiency and value for application. This study aimed to establish a convenient and prompt one for monitoring emerging infectious diseases online and achieving risk assessment in real time.MethodsThe Optimized Moving Average Prediction Limit (Op-MAPL) algorithm model analysed real-time COVID-19 data online and was validated using the data of the Delta variant in India and the Omicron in the United States. Then, the model was utilized to determine the infection risk level of the Omicron in Shanghai and Beijing.ResultsThe Op-MAPL model can predict the epidemic peak accurately. The daily risk ranking was stable and predictive, with an average accuracy of 87.85% within next 7 days. Early warning signals were issued for Shanghai and Beijing on February 28 and April 23, 2022, respectively. The two cities were rated as medium–high risk or above from March 27 to April 20 and from April 24 to May 5, indicating that the pandemic had entered a period of rapid increase. After April 21 and May 26, the risk level was downgraded to medium and became stable by the algorithm, indicating that the pandemic had been controlled well and mitigated gradually.ConclusionsThe Op-MAPL relies on nothing but an indicator to assess the risk level of the COVID-19 pandemic with different data sources and granularities. This forward-looking method realizes real-time monitoring and early warning effectively to provide a valuable reference to prevent and control infectious diseases.

Smart Healthy Schools: An IoT-enabled concept for multi-room dynamic air quality control

Smart Healthy Schools (SHS) are a new paradigm in building engineering and infection risk control in school buildings where the disciplines of Indoor Air Quality (IAQ), IoT (Internet of Things) and Artificial Intelligence (AI) merge together. In the post-pandemic era, equipping schools with a network of smart IoT sensors has become critical to aspire for the optimal control of the IAQ and lowering the airborne infection risk of several pathogens, indirectly related to cumulated human emitted CO2 levels over time. Thermal energy waste in winter due to improved air renewal remains of major concern but can be well monitored within a SHS monitoring architecture thanks to the flexibility of the LoRaWAN protocol able to process also a large amount of energy and climatic data at room and building scale. In this work, we report the design of the AulaSicura platform, an IoT control system co-designed by the main author and Gizero Energie to implement the SHS paradigm via clearly visible (and audible) alarm signalling in existing and new school buildings. The cloud-based LoRa system is capable of continuous and simultaneous monitoring of a variety of sensors and IAQ parameters including indoor/oudoor temperatures, rel. humidities and human-emitted excess CO2. The multi-room monitoring concept of indoor-CO2 levels allows centralized control of natural ventilation levels in individual classrooms and can handle (quasi)-real-time data, relevant for data post-processing and future developments in (quasi)-real-rime assessment of IAQ and infection risk levels at single room scale. The sensor network is also extensible to up to one thousand of classrooms per LoRa-node allowing centralized control of entire school districts at an urban scale. Moreover, through Modbus-LoRa I/O converters, AulaSicura can also control the same amount of mechanical ventilation units per node either in pure or hybrid mechanical ventilation modes.

Risk-based allocation of COVID-19 personal protective equipment under supply shortages

The COVID-19 outbreak put healthcare systems across the globe under immense pressure to meet the unprecedented demand for critical supplies and personal protective equipment (PPE). The traditional cost-effective supply chain paradigm failed to respond to the increased demand, putting healthcare workers (HCW) at a much higher infection risk relative to the general population. Recognizing PPE shortages and high infection risk for HCWs, the World Health Organization (WHO) recommends allocations based on ethical principles. In this paper, we model the infection risk for HCWs as a function of usage and use it as the basis for distribution planning that balances government procurement decisions, hospitals’ PPE usage policies, and WHO ethical allocation guidelines. We propose an infection risk model that integrates PPE allocation decisions with disease progression estimates to quantify infection risk among HCWs. The proposed risk function is used to derive closed-form allocation decisions under WHO ethical guidelines in both deterministic and stochastic settings. The modelling is then extended to dynamic distribution planning. Although nonlinear, we reformulate the resulting model to make it solvable using off-the-shelf software. The risk function successfully accounts for virus prevalence in space and in time and leads to allocations that are sensitive to the differences between regions. Comparative analysis shows that the allocation policies lead to significantly different levels of infection risk, especially under high virus prevalence. The best-outcome allocation policy that aims to minimize the total infected cases outperforms other policies under this objective and that of minimizing the maximum number of infections per period.

Effects of circuit exercise training strategy on health-related physical fitness level and biomarkers in elderly people with cardiovascular diseases

This study investigated the effects of a 12-week circuit exercise training intervention on health-related physical fitness and biomarkers in elderly adults with cardiovascular diseases. A total of 69 participants underwent the circuit exercise training program, which included stretching, aerobic activity, and weight training. The results showed that the participants had improved performance in cardiovascular and strength tests, with grip and back muscle strength showing improvement. However, sit-ups were lower than the average, and power, reaction time, and agility needed more effort. The high sensitivity C-reactive protein value reached a medium infection risk level for cardiovascular illnesses. The study concluded that there was a strong association between health-related physical fitness and biomarker index in senior persons with a medium risk cluster of cardiovascular illnesses. The study also found that BMI correlated positively with biomarker index, while cardiovascular fitness, agility, and flexibility correlated negatively with biomarker index.

Population flow based spatial-temporal eigenvector filtering modeling for exploring effects of health risk factors on COVID-19

The COVID-19 pandemic has had great impact on human health and social economy. Several studies examined spatial and temporal patterns of health risk factors associated with COVID-19, but population flow spillover effect has not been sufficiently considered. In this paper, a population flow-based spatial-temporal eigenvector filtering model (FLOW-ESTF) was developed to consider spatial-temporal patterns and population flow connectivity simultaneously. The proposed FLOW-ESTF method efficiently improved model prediction accuracy, which could help the government aware of the infection risk level and to make suitable control policies. The selected population flow spatial-temporal eigenvector contributed most to modeling and the visualization of corresponding eigenvector set helped to explore the underlying spatial-temporal patterns and pandemic transmission nodes. The model coefficients could reflect how health risk factors contribute the modeling of state-level COVID-19 weekly increased cases and how their influence changed through time, which could help people and government to better aware the potential health risks and to adjust control measures at different stage. The extracted population flow spatial-temporal eigenvector not only represents influence of population flow and its spillover effects but also represents some possible omitted health risk factors. This could provide an efficient path to solve the problem of spatial and temporal autocorrelation in COVID-19 modeling and an intuitive way to discover underlying spatial patterns, which will partially compensate for the problems of insufficient consideration of potential risk variables and missing data.

Assessment of the risk of Legionella pneumophila in water distribution systems in hospitals of Tehran city.

When a sensitive host inhales aerosols containing these bacteria, Legionella infection occurs. Therefore, monitoring and assessing Legionella in the environment and water distribution systems of such places are critical due to the prone population in hospitals. However, the health risks of Legionella bacteria in the environment are not adequately evaluated. In this study, for hospitalized patients, we performed a quantitative health risk assessment of Legionella in selected hospitals in Tehran city using two scenarios of shower and toilet faucet exposure. This study identified Legionella in 38 cases (38%) out of 100 samples collected from toilet faucets and showers in 8 hospitals. The information gathered was used for quantitative microbial risk assessment (QMRA). The microbial load transmitted by inhalation was calculated using the concentration of Legionella in water. Other exposure parameters (inhalation rate and exposure time) were obtained using information from other studies and the median length of hospital stay (3.6days). The exponential model was used to estimate the risk of infection (γ = 0.06) due to Legionella pneumophila (L. pneumophila) inhalation for each exposure event. For the mean concentration obtained for Legionella (103CFU/L), the risk of infection for toilet faucets and showers was in the range of 0.23-2.3 and 3.5-21.9, respectively, per 10,000 hospitalized patients. The results were compared with the tolerable risk level of infection determined by the US EPA and WHO. The risk values exceeded the WHO values for waterborne pathogens in hospitals in both exposure scenarios. As a result, our QMRA results based on monitoring data showed that despite using treated water (from distribution networks in the urban areas) by hospitals, 38% of the samples were contaminated with Legionella, and faucets and showers can be sources of Legionella transmission. Hence, to protect the health of hospitalized patients, the risk of Legionella infection should be considered.

Personal space increases during the COVID-19 pandemic in response to real and virtual humans.

Personal space is the distance that people tend to maintain from others during daily life in a largely unconscious manner. For humans, personal space-related behaviors represent one form of non-verbal social communication, similar to facial expressions and eye contact. Given that the changes in social behavior and experiences that occurred during the COVID-19 pandemic, including “social distancing” and widespread social isolation, may have altered personal space preferences, we investigated this possibility in two independent samples. First, we compared the size of personal space measured before the onset of the pandemic to its size during the pandemic in separate groups of subjects. Personal space size was significantly larger in those assessed during (compared to those assessed before) the onset of the pandemic (all d > 0.613, all p < 0.007). In an additional cohort, we measured personal space size, and discomfort in response to intrusions into personal space, longitudinally before and during the pandemic, using both conventional and virtual reality-based techniques. Within these subjects, we found that measurements of personal space size with respect to real versus virtual humans were significantly correlated with one another (r = 0.625–0.958) and similar in magnitude. Moreover, the size of personal space, as well as levels of discomfort during personal space intrusions, increased significantly during (compared to before) the COVID-19 pandemic in response to both real and virtual humans (all d > 0.842, all p < 0.01). Lastly, we found that the practice of social distancing and perceived (but not actual) risk of being infected with COVID-19 were linked to this personal space enlargement during the pandemic (all p < 0.038). Taken together, these findings suggest that personal space boundaries expanded during the COVID-19 pandemic independent of actual infection risk level. As the day-to-day effects of the pandemic subside, personal space preferences may provide one index of recovery from the psychological effects of this crisis.

A mathematical model for equitable in-country COVID-19 vaccine allocation

Given the scarcity of COVID-19 vaccines, equitable (fair) allocation of limited vaccines across the main administrative units of a country (e.g. municipalities) has been an important concern for public health authorities worldwide. In this study, we address the equitable allocation of the COVID-19 vaccines inside countries by developing a novel, evidence-based mathematical model that accounts for multiple priority groups (e.g. elderly, healthcare workers), multiple vaccine types, and regional characteristics (e.g. storage capacities, infection risk levels). Our research contributes to the literature by developing and validating a model that proposes equitable vaccine allocation alternatives in a very short time by (a) minimising deviations from the so-called ‘fair coverage’ levels that are computed based on weighted pro-rata rations, and (b) imposing minimum coverage thresholds to control the allocation of vaccines to higher priority groups and regions. To describe the merits of our model, we provide several equity and effectiveness metrics, and present insights on different allocation policies. We compare our methodology with similar models in the literature and show its better performance in achieving equity. To illustrate the performance of our model in practice, we perform a comprehensive numerical study based on actual data corresponding to the early vaccination period in Turkey.

Assessing COVID-19 risk with temporal indices and geographically weighted ordinal logistic regression in US counties.

PurposeResearch on the novel coronavirus diseases 2019 (COVID-19) mainly relies on cross-sectional data, but this approach fails to consider the temporal dimension of the pandemic. This study assesses three temporal dimensions of the COVID-19 infection risk in US counties, namely probability of occurrence, duration of the pandemic, and intensity of transmission, and investigate local patterns of the factors associated with these risks.MethodsAnalyzing daily data between January 22 and September 11, 2020, we categorize the contiguous US counties into four risk groups—High-Risk, Moderate-Risk, Mild-Risk, and Low-Risk—and then apply both conventional (i.e., non-spatial) and geographically weighted (i.e., spatial) ordinal logistic regression model to understand the county-level factors raising the COVID-19 infection risk. The comparisons of various model fit diagnostics indicate that the spatial models better capture the associations between COVID-19 risk and other factors.ResultsThe key findings include (1) High- and Moderate-Risk counties are clustered in the Black Belt, the coastal areas, and Great Lakes regions. (2) Fragile labor markets (e.g., high percentages of unemployed and essential workers) and high housing inequality are associated with higher risks. (3) The Monte Carlo tests suggest that the associations between covariates and COVID-19 risk are spatially non-stationary. For example, counties in the northeastern region and Mississippi Valley experience a stronger impact of essential workers on COVID-19 risk than those in other regions, whereas the association between income ratio and COVID-19 risk is stronger in Texas and Louisiana.ConclusionsThe COVID-19 infection risk levels differ greatly across the US and their associations with structural inequality and sociodemographic composition are spatially non-stationary, suggesting that the same stimulus may not lead to the same change in COVID-19 risk. Potential interventions to lower COVID-19 risk should adopt a place-based perspective.

Basiliximab for steroid‐refractory acute graft‐versus‐host disease: A real‐world analysis

Steroid-refractory (SR) acute graft-versus-host disease (aGVHD) is one of the leading causes of early mortality after allogeneic hematopoietic stem cell transplantation (allo-HSCT). We investigated the efficacy, safety, prognostic factors, and optimal therapeutic protocol for SR-aGVHD patients treated with basiliximab in a real-world setting. Nine hundred and forty SR-aGVHD patients were recruited from 36 hospitals in China, and 3683 doses of basiliximab were administered. Basiliximab was used as monotherapy (n=642) or in combination with other second-line treatments (n=298). The cumulative incidence of overall response rate (ORR) at day 28 after basiliximab treatment was 79.4% (95% confidence interval [CI] 76.5%-82.3%). The probabilities of nonrelapse mortality and overall survival at 3 years after basiliximab treatment were 26.8% (95% CI 24.0%-29.6%) and 64.3% (95% CI 61.2%-67.4%), respectively. A 1:1 propensity score matching was performed to compare the efficacy and safety between the monotherapy and combined therapy groups. Combined therapy did not increase the ORR; conversely, it increased the infection rates compared with monotherapy. The multivariate analysis showed that combined therapy, grade III-IV aGVHD, and high-risk refined Minnesota aGVHD risk score before basiliximab treatment were independently associated with the therapeutic response. Hence, we created a prognostic scoring system that could predict the risk of having a decreased likelihood of response after basiliximab treatment. Machine learning was used to develop a protocol that maximized the efficacy of basiliximab while maintaining acceptable levels of infection risk. Thus, real-world data suggest that basiliximab is safe and effective for treating SR-aGVHD.

An optimization model for planning testing and control strategies to limit the spread of a pandemic – The case of COVID-19

The global health crisis caused by the coronavirus SARS-CoV-2 has highlighted the importance of efficient disease detection and control strategies for minimizing the number of infections and deaths in the population and halting the spread of the pandemic. Countries have shown different preparedness levels for promptly implementing disease detection strategies, via mass testing and isolation of identified cases, which led to a largely varying impact of the outbreak on the populations and health-care systems. In this paper, we propose a new pandemic resource allocation model for allocating limited disease detection and control resources, in particular testing capacities, in order to limit the spread of a pandemic. The proposed model is a novel epidemiological compartmental model formulated as a non-linear programming model that is suitable to address the inherent non-linearity of an infectious disease progression within the population. A number of novel features are implemented in the model to take into account important disease characteristics, such as asymptomatic infection and the distinct risk levels of infection within different segments of the population. Moreover, a method is proposed to estimate the vulnerability level of the different communities impacted by the pandemic and to explicitly consider equity in the resource allocation problem. The model is validated against real data for a case study of COVID-19 outbreak in France and our results provide various insights on the optimal testing intervention time and level, and the impact of the optimal allocation of testing resources on the spread of the disease among regions. The results confirm the significance of the proposed modeling framework for informing policymakers on the best preparedness strategies against future infectious disease outbreaks.

Message Delivery Strategy Influences Willingness to Comply With Biosecurity

As the Covid-19 pandemic continues worldwide, it has become increasingly clear that effective communication of disease transmission risks associated with protective behaviors is essential, and that communication tactics are not ubiquitously and homogenously understood. Analogous to Covid-19, communicable diseases in the hog industry result in millions of animal deaths and in the United States costs hundreds of millions of dollars annually. Protective behaviors such as preventative biosecurity practices are implemented to reduce these costs. Yet even with the knowledge of the importance of biosecurity, these practices are not employed consistently. The efficacy of biosecurity practices relies on consistent implementation and is influenced by a variety of behavioral factors under the umbrella of human decision-making. Using an experimental game, we collected data to quantify how different messages that described the likelihood of a disease incursion would influence willingness to follow biosecurity practices. Here we show that graphical messages combined with linguistic phrases demarking infection risk levels are more effective for ensuring compliance with biosecurity practices, as contrasted with either simple linguistic phrases or graphical messages with numeric demarcation of risk levels. All three of these delivery methods appear to be more effective than using a simple numeric value to describe probability of infection. Situationally, we saw greater than a 3-fold increase in compliance by shifting message strategy without changing the infection risk, highlighting the importance of situational awareness and context when designing messages.

Ecological aspects of zoning the territory of the Saratov region by the risk level of formation of West Nile fever foci

The modern period is characterized by the expansion of the areas of a number of dangerous infectious diseases, previously endemic only for the countries with tropical climate. As a result of Palearctic climate warming and humidification and under the influence of anthropogenic factors, foci of a new transmissible arboviral zoonosis – West Nile fever (WNF) – have been formed in the Saratov region. On the territory of Russia, the circulation of the West Nile virus (WNV) has been recorded since 1963, and cases of human infection have been detected since 1967. In the studied region, the circulation of the virus has been known since the mid-90s of the last century, and epidemic complications – since 2012. 142 cases of the disease were reported in 2012–2020. The ecological prerequisites for a wide circulation of WNV are associated with the increase in the number of birds – carriers, and arthropods – blood-sucking vectors, the longer activity period of mosquitoes and ticks owing to the reduced frosty season of the year. As a result of landscape mapping with the use of modern methods for decoding images of satellite maps and remote sensing of the Earth (ERS), data have been obtained that served as the basis for epidemiological WNF zoning. There are three types of potential WNF foci on the territory of the region, namely: natural, natural-anthropourgic, and anthropourgic ones. The total area of biocenoses has been established, where natural, natural-anthropourgic and anthropourgic WNF foci could be formed: 6619.94 km2, 1484.62 km2, and 70.4 km2, respectively. Cluster analysis of the environmental conditions in 38 administrative districts of the region has distinguished four groups differing in the risk level of infection of the population with West Nile fever. The data obtained are used for planning, substantiating and conducting surveys and preventive measures, and form the basis for predicting the epidemiological situation in the region.