The susceptible-infected-removed (SIR) model and its variants are widely used to predict the progress of coronavirus disease 2019 (COVID-19) worldwide, despite their rather simplistic nature. Nevertheless, robust estimation of the SIR model presents a significant challenge, particularly with limited and possibly noisy data in the initial phase of the pandemic.

The K-means algorithm is used to perform a cluster analysis of the top 10 countries with the highest number of COVID-19 cases, to observe if there are any significant differences among countries in terms of robustness.

As a result of model variation tests, the robustness of parameter estimates is found to be particularly problematic in developing countries. The incompatibility of parameter estimates with the observed characteristics of COVID-19 is another potential problem. Hence, a series of research questions are visited.

We propose a Single Parameter Estimation (SPE) approach to circumvent these potential problems if the basic SIR is the model of choice, and we check the robustness of this new approach by model variation and structured permutation tests. Dissemination of quality predictions is critical for policy- and decision-makers in shedding light on the next phases of the pandemic.

Wearing face masks is believed to mitigate coronavirus disease 2019 (COVID-19) virus transmission by filtering respiratory droplets. This study was to explore the factors influencing wearing face masks in public in China during COVID-19 outbreak.

This study was a qualitative semi-structured interview research design and was guided by the Protection Motivation Theory. Participants from Jiangxi Province China were interviewed by means of WeChat video call. Thematic analysis was used to analyze the data.

Recruitment efforts were suspended when 21 participants (aged 23 to 72 y) were successfully enrolled and the data reached thematic saturation. Four themes were identified when participants described factors influencing them to wear face masks: knowledge of disease (subthemes were severity of disease, and individual vulnerability to disease), environmental facilitators and constraints (subthemes were government recommendations, public opinion, and affordability and availability of face masks), understanding of protection effectiveness (subthemes were protection effectiveness of wearing face masks, and selection of protective measures), and past experiences.

Individuals’ decision to wear face masks was influenced by the combination of factors identified. Identification of these factors provides guidance for explaining wearing face masks in public and helps policy-makers develop feasible recommendations for wearing face masks during COVID-19 outbreak.

A danger threatening hospitals is fire. The most important action following a fire is to urgently evacuate the hospital during the shortest time possible. The aim of this study was to predict the duration of emergency evacuation following hospital fire using machine-learning algorithms.

In this study, the real emergency evacuation duration of 190 patients admitted to a hospital was predicted in a simulation based on the following 8 factors: the number of hospital floors, patient preparation and transfer time, distance to the safe location, as well as patient’s weight, age, sex, and movement capability. To design and validate the model, we used statistical models of machine learning, including Support Vector Machines Random Forest, Naive Bayes Classifier, and Artificial Neural Network.

Data analysis showed that based on the Area Under the Curve, precision, and sensitivity values of 99.5%, 92.4%, and 92.1%, respectively, the Random Forest model showed a better performance compared to other models for predicting the duration of hospital emergency evacuation during fire.

Predicting evacuation duration can provide managers with accurate information and true analyses of these events. Therefore, health policy makers and managers can promote preparedness and responsiveness during fire by predicting evacuation duration and developing appropriate plans using machine learning models.

This research evaluated the resilience of 6 tertiary and rural health facilities within a single Australian Health Service, using the World Health Organization (WHO) Hospital Safety Index (HSI). This adaptation of the HSI was compared with existing national accreditation and facility design Standards to assess disaster preparedness and identify opportunities for improvement.

This cross-sectional descriptive study surveyed 6 hospitals that provide 24/7 emergency department and acute inpatient services. HSI assessments, comprising 151 previously validated criteria, were conducted by Health Service engineers and facility managers before being externally reviewed by independent disaster management professionals.

All facilities were found to be highly disaster resilient, with each recording high HSI scores. Variances in structure, architectural safety, continuity of critical services supply, and emergency plans were consistently identified. Power and water supply vulnerabilities are common to previously reported vulnerabilities in health facilities of developing countries.

Clinical, engineering, and disaster management professionals assessed 6 Australian hospitals using the WHO HSI with each facility scoring highly, genuine vulnerabilities and practical opportunities for improvement were identified. This application of the WHO HSI, intended for use primarily in developing countries and disaster-affected regions, complimented and extended the existing Australian national health service accreditation and facility design Standards. These results support the expansion of existing assessment tools used to assess Australian health facility disaster preparedness and resilience.

Respiratory disease vaccines may affect coronavirus disease 2019 (COVID-19) - associated infection and mortality rates due to vaccine nonspecific effects against viral infections. We compared the infection and mortality rates in relation to COVID-19 between countries with and without universal respiratory disease vaccine policies.

In this ecological study, 186 countries with COVID-19 statistics from the World Health Organization (WHO) were included.

The study found that countries with universal BCG (bacillus Calmette Guérin) vaccine had significantly lower total infection and mortality rates, 0.2979 and 0.0077 versus 3.7445, and 0.0957/1000 people and confirmed cases (P < 0.001). The countries with universal pneumococcal vaccine (PCV), including PCV1, PCV2, and PCV3 vaccines, had significantly higher total mortality, 0.0111 versus 0.0080, respectively (P = 0.032). Higher income was associated with increasing total infection and mortality rates. Whereas, BCG vaccination was associated with a lower total mortality rate only (P = 0.030). The high-income countries were more likely to not receive universal BCG and receive second dose of meningococcal conjugate vaccine (MCV2) and third dose of PCV3 vaccination coverage. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection rates increased with increasing years of the second dose of measles-containing vaccine (P = 0.026) and pneumococcal conjugate third dose (PCV3).

This study suggests that BCG vaccination could reduce the infection caused by COVID-19, and MCV2 vaccine years increases the total infection rate. This study identified high economic characteristics and not having universal BCG coverage as the independent risk factors of mortality by multivariate analysis.

This report aimed to examine the literature regarding evidence about community-based interventions that use the concept of resilience to increase positive health outcomes after disaster.

A search was conducted of databases; gray literature, public health journals, and available key journals focused on disaster, emergency, and trauma from inception to December 2013. Excluded were non-English publications, only about children or adolescents, or a commentary or theoretical discussion on resilience.

From a total of 1880 records, excluding duplicates, 8 studies were found. Exclusions included participants younger than age 18 years (n=74), non-English (n=40), nonempirical (n=265), not referring to disaster (n=188), not a public health intervention (n=319), and not related to an intervention targeting resilience (n=890).

This systematic review highlighted a gap in the evidence relating to interventions targeting the resilience of adults who have experienced a disaster. The results were mixed in relation to information provision but promising for strategies that promote social interactions or develop community competence. Future studies could explore the ability of interventions to build the intrinsic capacity of a system, community, or society at risk of a disaster to adapt and survive. (Disaster Med Public Health Preparedness. 2014;0:1-9)

Emergency departments (EDs) are critical sources of care after natural disasters such as hurricanes. Understanding the impact on ED utilization by subpopulation and proximity to the hurricane’s path can inform emergency preparedness planning. This study examines changes in ED utilization for residents of 344 counties after the occurrence of 7 US hurricanes between 2005 and 2016.

This retrospective observational study used ED data from the Healthcare Cost and Utilization Project State Inpatient Databases and State Emergency Department Databases. ED utilization rates for weeks during and after hurricanes were compared with pre-hurricane rates, stratified by the proximity of the patient county to the hurricane path, age, and disease category.

The overall population rate of weekly ED visits changed little post-hurricane, but rates by disease categories and age demonstrated varying results. Utilization rates for respiratory disorders exhibited the largest post-hurricane increase, particularly 2–3 weeks following the hurricane. The change in population rates by disease categories and age tended to be larger for people residing in counties closer to the hurricane path.

Changes in ED utilization following hurricanes depend on disease categories, age, and proximity to the hurricane path. Emergency managers could incorporate these factors into their planning processes.

The aim of this study was to evaluate hospital and emergency department (ED) preparedness in France facing the coronavirus disease 2019 (COVID-19) rapid growth epidemic-phase, and to determine the link between preparedness and responsiveness.

In this cross-sectional study, from March 7 to March 11, 2020, all heads of ED departments in France were contacted to answer an electronic survey, including 23 questions. Quality, Organization, Training, Resources, Management, Interoperability, and Responsiveness were evaluated by calculating scores (10 points). Multivariate analysis of variance was used to compare scores. Spearman’s correlation coefficient and multifaceted regression analysis were performed between Responsiveness and dimensions scores.

A total of 287 of 636 French EDs were included (45.1%). Calculated scores showed (median): Quality 5.38; Organization 6.4; Training 4.6; Resources 4.13; Management 2.38; Interoperability 4.0; Responsiveness 6.25; seasonal influenza score was 5. Significant differences between scores as a function of hospital and ED main characteristics were found. Furthermore, we found significant correlations (P < 0.01) between Responsiveness and all preparedness dimensions. Organization (adjusted-R2 0.2897), Management (aR2 0.321), and Interoperability (aR2 0.422) were significantly associated with Responsiveness.

Preparedness in all its dimensions is low, indicating vulnerability. Preparedness and responsiveness face a certain and ongoing risk are close linked, and that Organizational, Management, and Interoperability dimensions are main determinants.

The participants in a 2011 drive-through influenza vaccination clinic were described.

The descriptive, cross-sectional study used a pen-and-paper survey administered during a drive-through vaccination program. A total of 1114 surveys were collected that included demographic information and responses to 3 clinic usage questions; responses were in English or Spanish. The responses were stratified by language and age group, and zip codes reported by the participants were mapped.

The majority of the participants were women (57%) aged 41 to 64 years old (53%). The participation by people younger than 18 and older than 65 years was relatively low. When compared by language, the surveys completed in Spanish showed a significantly different proportion of age group participation than those completed in English. Many participants (23%) indicated that they likely would not have received a vaccine elsewhere if the clinic were not available. A map of the zip codes showed that the clinic served people from the city and surrounding communities and counties.

The operators of vaccine clinics can use these findings to improve outreach to populations of concern, and encourage multiple venues, both traditional and nontraditional, to maximize immunization coverage in their community (Disaster Med Public Health Preparedness. 2014;0:1-4).

Previous research suggests that people with asthma may experience a worsening of symptoms following hurricanes due to changes in environmental exposures, discontinuity in chronic disease management, and stress. The objective of this study was to estimate changes in asthma-related emergency department (ED) visits in North Carolina following Hurricane Irene, which made landfall in August 2011.

Changes in asthma-related ED visits in September to December of 2010 and 2011 were examined using data from the Healthcare Cost and Utilization Project State Emergency Department and Inpatient Databases. A Poisson generalized linear model was used to estimate the association between Federal Emergency Management Agency disaster declarations following Hurricane Irene and county-level asthma-related ED visits controlling for month, year, and county.

Following Hurricane Irene, disaster declarations were made for 38 of 100 counties in North Carolina. In September 2010, the rate of asthma-related ED visits for North Carolina was 6 per 10,000 person-months. In September 2011, rates of asthma-related ED visits were similar in counties with and without disaster declarations (7 and 5 per 10,000 person-months, respectively). When adjusting for covariates, there was little or no difference in the rate of asthma ED visits before and after the hurricane between counties with and without a disaster declaration (rate ratio {RR} [95% confidence interval {CI}] = 1.02[0.97, 1.08]).

Although risk factors for asthma exacerbations increase following hurricanes, these results found little evidence of an increase in asthma-related ED visits in North Carolina following Hurricane Irene.