BACKGROUND: To control resurging infectious diseases like mumps, it is necessary to resort to effective control and preventive measures. These measures include increasing vaccine coverage, providing the community with advice on how to reduce exposure, and closing schools. To justify such intervention, it is important to understand how well each of these measures helps to limit transmission.
METHODS: In this paper, we propose a simple SEILR (susceptible-exposed-symptomatically infectious-asymptomatically infectious-recovered) model by using a novel transmission rate function to incorporate temperature, humidity, and closing school factors. This new transmission rate function allows us to verify the impact of each factor either separately or combined. Using reported mumps cases from 2004 to 2018 in the mainland of China, we perform data fitting and parameter estimation to evaluate the basic reproduction number  R 0 . As a wide range of one-dose measles, mumps, and rubella (MMR) vaccine programs in China started only in 2008, we use different vaccination proportions for the first Stage I period (from 2004 to 2008) and the second Stage II period (from 2009 to 2018). This allows us to verify the importance of higher vaccine coverage with a possible second dose of MMR vaccine.
RESULTS: We find that the basic reproduction number  R 0  is generally between 1 and 3. We then use the Akaike Information Criteria to assess the extent to which each of the three factors contributed to the spread of mumps. The findings suggest that the impact of all three factors is substantial, with temperature having the most significant impact, followed by school opening and closing, and finally humidity.
CONCLUSIONS: We conclude that the strategy of increasing vaccine coverage, changing micro-climate (temperature and humidity), and closing schools can greatly reduce mumps transmission.

Amorphous solid dispersions (ASDs) represent an important approach for enhancing oral bioavailability for poorly water soluble compounds; however, assuring that these ASDs do not recrystallize to a significant extent during storage can be time-consuming. Therefore, various efforts have been undertaken to predict ASD crystallization levels with kinetic models. However, only limited success has been achieved due to limits on crystal content quantification methods and the complexity of crystallization kinetics. To increase the prediction accuracy, the accelerated stability assessment program (ASAP), employing isoconversion (time to hit a specification limit) and a modified Arrhenius approach, are employed here for predictive shelf-life modeling. In the current study, a model ASD was prepared by spray drying griseofulvin and HPMC-AS-LF. This ASD was stressed under a designed combinations of temperature, relative humidity and time with the conditions set to ensure stressing was carried out below the glass transition temperature (Tg) of the ASD. Crystal content quantification method by X-ray powder diffraction (XRPD) with sufficient sensitivity was developed and employed for stressed ASD. Crystallization modeling of the griseofulvin ASD using ASAPprime® demonstrated good agreement with long-term (40 °C/75 %RH) crystallinity levels and support the use of this type of accelerated stability studies for further improving ASD shelf-life prediction accuracy.

To adapt to Earth\'s rapidly changing climate, detailed modelling of thermal stress is needed. Dangerous stress levels are becoming more frequent, longer, and more severe. While traditional measurements of thermal stress have focused on air temperature and humidity, modern measures including radiation and wind speed are becoming widespread. However, projecting such indices has presented a challenging problem, due to the need for appropriate bias correction of multiple variables that vary on hourly timescales. In this paper, we aim to provide a detailed understanding of changing thermal stress patterns incorporating modern measurements, bias correction techniques, and hourly projections to assess the impact of climate change on thermal stress at human scales. To achieve these aims, we conduct a case study of projected thermal stress in central Hobart, Australia for 2040-2059, compared to the historical period 1990-2005. We present the first hourly metre-scale projections of thermal stress driven by multivariate bias-corrected data. We bias correct four variables from six dynamically downscaled General Circulation Models. These outputs drive the Solar and LongWave Environmental Irradiance Geometry model at metre scale, calculating mean radiant temperature and the Universal Thermal Climate Index. We demonstrate that multivariate bias correction can correct means on multiple time scales while accurately preserving mean seasonal trends. Changes in mean air temperature and UTCI by hour of the day and month of the year reveal diurnal and annual patterns in both temporal trends and model agreement. We present plots of future median stress values in the context of historical percentiles, revealing trends and patterns not evident in mean data. Our modelling illustrates a future Hobart that experiences higher and more consistent numbers of hours of heat stress arriving earlier in the year and extending further throughout the day.

The COVID-19 pandemic originated in 2019 and has become an endemic disease that we must learn to live with, similar to other strains of influenza. The Organization (WHO) declared on May 5, 2023, in Geneva, Switzerland, the end of the Public Health Emergency of International Concern regarding COVID-19. As vaccines become more widely available and the pandemic appears to be improved, our focus shifts to the challenges we still face. Understanding how external factors like temperature, air humidity, and social isolation impact the spread of the SARS-CoV-2 virus remains a crucial challenge beyond our control. In this study, potential links between the number of COVID-19 cases in São Paulo City (SPC) and New York City (NWC) were explored. Our analysis was carried out utilizing the continuous wavelet transform, alongside other tools such as cross-wavelet transform and wavelet coherence. Based on our findings, there appears to be a correlation between the variables related to low frequencies, which aligns with previous research on the topic. Particularly, our research has revealed a connection between COVID-19 cases and factors such as temperature, air humidity, and social isolation rates. Regarding the latter, our findings indicate that implementing social distancing measures was a wise public policy decision, although the correlation with daily COVID-19 cases requires careful analysis. For this study, we analyzed data from February of 2020, when the first cases were reported in the cities under investigation, SPC and NWC, up until December 31, 2022, by which time the vaccination campaign was well under way.

Humans are exposed to diverse communities of microbes every day. With more time spent indoors by humans, investigations into the communities of microbes inhabiting occupied spaces have become important to deduce the impacts of these microbes on human health and building health. Studies so far have given considerable insight into the communities of the indoor microbiota humans interact with, but mainly focus on sampling surfaces or indoor dust from filters. Beneath the surfaces though, building envelopes have the potential to contain environments that would support the growth of microbial communities. But due to design choices and distance from ground moisture, for example, the temperature and humidity across a building will vary and cause environmental gradients. These microenvironments could then influence the composition of the microbial communities within the walls. Here we present a case study designed to quantify any patterns in the compositions of fungal and bacterial communities existing in a building envelope and determine some of the key variables, such as cardinal direction, distance from floor or distance from wall joinings, that may influence any microbial community composition variation. By drilling small holes across walls of a house, we extracted microbes onto air filters and conducted amplicon sequencing. We found sampling height (distance from the floor) and cardinal direction the wall was facing caused differences in the diversity of the microbial communities, showing that patterns in the microbial composition will be dependent on sampling location within the building. By sampling beneath the surfaces, our approach provides a more complete picture of the microbial condition of a building environment, with the significant variation in community composition demonstrating a potential sampling bias if multiple sampling locations across a building are not considered. By identifying features of the built environment that promote/retard microbial growth, improvements to building designs can be made to achieve overall healthier occupied spaces.

BACKGROUND: Evidence is limited regarding the association between meteorological factors and COVID-19 transmission in low- and middle-income countries (LMICs).
OBJECTIVE: To investigate the independent and interactive effects of temperature, relative humidity (RH), and ultraviolet (UV) radiation on the spread of COVID-19 in LMICs.
METHODS: We collected daily data on COVID-19 confirmed cases, meteorological factors and non-pharmaceutical interventions (NPIs) in 2143 city- and district-level sites from 6 LMICs during 2020. We applied a time-stratified case-crossover design with distributed lag nonlinear model to evaluate the independent and interactive effects of meteorological factors on COVID-19 transmission after controlling NPIs. We generated an overall estimate through pooling site-specific relative risks (RR) using a multivariate meta-regression model.
RESULTS: There was a positive, non-linear, association between temperature and COVID-19 confirmed cases in all study sites, while RH and UV showed negative non-linear associations. RR of the 90th percentile temperature (28.1 °C) was 1.14 [95% confidence interval (CI): 1.02, 1.28] compared with the 50th percentile temperature (24.4 °C). RR of the10th percentile UV was 1.41 (95% CI: 1.29, 1.54). High temperature and high RH were associated with increased risks in temperate climate but decreased risks in tropical climate, while UV exhibited a consistent, negative association across climate zones. Temperature, RH, and UV interacted to affect COVID-19 transmission. Temperature and RH also showed higher risks in low NPIs sites.
CONCLUSIONS: Temperature, RH, and UV appeared to independently and interactively affect the transmission of COVID-19 in LMICs but such associations varied with climate zones. Our results suggest that more attention should be paid to meteorological variation when the transmission of COVID-19 is still rampant in LMICs.

Due to the unique geographical location and historical culture, the traditional houses in the southern region of Anhui Province, China (South Anhui) have different indoor environments. In summer and winter, this study adopted a field survey, questionnaire survey, and statistical analysis to carry out a comprehensive field survey on Xixinan Village in South Anhui, and selected a typical traditional residence in the village to evaluate its indoor environment status. The final results show that the overall indoor environment of the traditional houses in South Anhui was awful, including the indoor thermal environment, with high temperature and humidity in summer and cold and humidity in winter. Additionally, the indoor light environment with dim light still had much room for improvement, while the indoor air quality and sound environment were relatively excellent. In addition, this study determined that the neutral temperatures of residents are 15.5 °C and 28.7 °C in winter and summer, respectively, and the comfort range of indoor light intensity is 752.6-1252.5 lx, which determines the adjustment range of indoor environmental parameters for residents\' comfort needs. This paper\'s research methods and results provide a reference for the study of residential indoor environments in other regions with similar climatic conditions as South Anhui, and a theoretical basis for architects and engineers to enhance the indoor environment of traditional houses in this region.

BACKGROUND: The objective of this study was to examine associations of daily averages and daily variations in ambient relative humidity (RH), temperature, and PM2.5 on the obstructive sleep apnea (OSA) severity.
METHODS: A case-control study was conducted to retrospectively recruit 8628 subjects in a sleep center between January 2015 and December 2021, including 1307 control (apnea-hypopnea index (AHI) < 5 events/h), 3661 mild-to-moderate OSA (AHI of 5-30 events/h), and 3597 severe OSA subjects (AHI > 30 events/h). A logistic regression was used to examine the odds ratio (OR) of outcome variables (daily mean or difference in RH, temperature, and PM2.5 for 1, 7, and 30 days) with OSA severity (by the groups). Two-factor logistic regression models were conducted to examine the OR of RH with the daily mean or difference in temperature or PM2.5 with OSA severity. An exposure-response relationship analysis was conducted to examine the outcome variables with OSA severity in all, cold and warm seasons.
RESULTS: We observed associations of mean PM2.5 and RH with respective increases of 0.04-0.08 and 0.01-0.03 events/h for the AHI in OSA patients. An increase in the daily difference of 1 % RH increased the AHI by 0.02-0.03 events/h in OSA patients. A daily PM2.5 decrease of 1 μg/m3 reduced the AHI by 0.03 events/h, whereas a daily decrease in the RH of 1 % reduced the AHI by 0.03-0.04 events/h. The two-factor model confirmed the most robust associations of ambient RH with AHI in OSA patients. The exposure-response relationship in temperature and RH showed obviously seasonal patterns with OSA severity.
CONCLUSIONS: Short-term ambient variations in RH and PM2.5 were associated with changes in the AHI in OSA patients, especially RH in cold season. Reducing exposure to high ambient RH and PM2.5 levels may have protective effects on the AHI in OSA patients.

Dams are studied not only for potential advantages but also for possible environmental repercussions. The objective of this case study is to see the effects caused by Gomal Zam Dam on temperature, humidity and precipitation in their surroundings, both spatially and temporally. The meteorology parameters\' characteristics have been detected using two different approaches, Mann-Kendall statistical test and Sen\'s slope estimator. Data stations chosen were those that have been close to the dam and sufficiently far away from the dam. As a consequence, although temporal changes in mean heat were established, periodic and regional disparities in humidity readings were found.

Painful diabetic neuropathy (PDN) is a common complication of diabetes mellitus, which reduces the quality of life. However, the association between PDN and environmental factors, especially ambient humidity, remains unclear. Therefore, this study investigated the impact of extreme humidity events on PDN. Data on PDN-related hospital admissions to two tertiary hospitals in Hefei, China (2014-2019) were obtained. A distributed lag non-linear model with a case-crossover design was used to quantitatively estimate the effects of ambient humidity on PDN, and the results were stratified by sex and age. The 1st, 10th, 90th, and 99th percentiles of relative humidity (RHU) were defined as extreme humidity, and the average relative humidity (74.94%) was set as the reference value. Non-linear exposure-response curves between the RHU and PDN cases were obtained. Extreme humidity (92%) had a significant effect on PDN with a relative risk (RR) of 1.13 (95% confidence interval (CI): 1.01-1.26) on a particular day, which increased with the RHU (RR: 1.21, 95% CI: 1.02-1.45 at 98% extreme humidity). Stratification analysis showed that women (RR: 1.38, 95% CI: 1.07-1.77) and patients aged < 65 years (RR: 1.26, 95% CI: 1.01-1.57) were highly susceptible to this effect on the same day. The results suggest that extreme humidity is a crucial trigger for PDN onset in diabetes patients. Furthermore, the effects vary with sex and age. This study provides detailed evidence of the adverse effects of extreme weather on diabetes patients.