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Background Pollen is a well-established trigger of asthma and allergic rhinoconjunctivitis, yet key gaps in our understanding remain. These include knowledge of concentration thresholds for symptoms, exposure-response associations through time, and the potential for interactions with other environmental stressors such as air pollution. Smartphone technology offers an opportunity to address these challenges using large datasets that capture individual symptoms in real time. Methods We analysed 44,820 symptom reports logged by 2,272 users of the AirRater app over four years to evaluate associations between daily respiratory symptoms and atmospheric concentrations of pollen in Tasmania, Australia. We used case time series, a novel methodology developed for app-sourced data. We adjusted for seasonality and meteorology and tested for interactions with particulate pollution (PM2.5). Results There was a non-linear association between pollen concentrations and respiratory symptoms for up to three days following exposure. Risk ratios (RR) were greatest on the same day, for total pollen increased steeply to a RR of 1.31 (95% CI: 1.26-1.37) at a concentration of 50 grains/m3 before plateauing. Associations with individual pollen taxa showed similar non-linear trends. There was an interaction with PM2.5, with effect estimates significantly higher when PM2.5 was >50 µg/m3 (p for interaction < 0.001). Conclusions The association between respiratory symptoms and airborne pollen was non-linear, greatest in magnitude on the day of exposure, and synergistic with air pollution. Key messages Smartphone symptom tracking offers a useful means of assessing dose-response relationships in environmental epidemiology.

Pollen is a well-established trigger of asthma and allergic rhinitis, yet concentration-response relationships, lagged effects, and interactions with other environmental factors remain poorly understood. Smartphone technology offers an opportunity to address these challenges using large, multi-year datasets that capture individual symptoms and exposures in real time. We aimed to characterise associations between six pollen types and respiratory symptoms logged by users of the AirRater smartphone app in Tasmania, Australia. We analyzed 44,820 symptom reports logged by 2272 AirRater app users in Tasmania over four years (2015-2019). With these data we evaluated associations between daily respiratory symptoms and atmospheric pollen concentrations. We implemented Poisson regression models, using the case time series approach designed for app-sourced data. We assessed potentially non-linear and lagged associations with (a) total pollen and (b) six individual pollen taxa. We adjusted for seasonality and meteorology and tested for interactions with particulate air pollution (PM2.5). We found evidence of non-linear associations between total pollen and respiratory symptoms for up to three days following exposure. For total pollen, the same-day relative risk (RR) increased to 1.31 (95% CI: 1.26-1.37) at a concentration of 50 grains/m3 before plateauing. Associations with individual pollen taxa were also non-linear with some diversity in shapes. For all pollen taxa the same-day RR was highest. The interaction between total pollen and PM2.5 was positive, with risks associated with pollen significantly higher in the presence of high concentrations of PM2.5. Our results support a non-linear response between airborne pollen and respiratory symptoms. The association was strongest on the day of exposure and synergistic with particulate air pollution. The associations found with Dodonaea and Myrtaceae highlight the need to further investigate the role of Australian native pollen types in allergic respiratory disease.

Background Non-pharmaceutical interventions (NPIs), such as sanitary measures and travel restrictions, aimed at controlling the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), may affect the transmission dynamics of human respiratory syncytial virus (HRSV). We aimed to quantify the contribution of the sales of hand hygiene products and the number of international and domestic airline passenger arrivals on HRSV epidemic in Japan. Methods The monthly number of HRSV cases per sentinel site (HRSV activity) in 2020 was compared with the average of the corresponding period in the previous 6 years (from January 2014 to December 2020) using a monthly paired t-test. A generalized linear gamma regression model was used to regress the time-series of the monthly HRSV activity against NPI indicators, including sale of hand hygiene products and the number of domestic and international airline passengers, while controlling for meteorological conditions (monthly average temperature and relative humidity) and seasonal variations between years (2014–2020). Results The average number of monthly HRSV case notifications in 2020 decreased by approximately 85% (p p = 0.003) decrease in HRSV infections. An increase of average 1000 domestic and international airline passenger arrivals during the previous 1–2 months was associated with a 3.8 × 10− 4% (p − 3% (p Conclusions This study suggests that there is an association between the decrease in the monthly number of HRSV cases and improved hygiene and sanitary measures and travel restrictions for COVID-19 in Japan, indicating that these public health interventions can contribute to the suppression of HRSV activity. These findings may help in public health policy and decision making.

Background Non-pharmaceutical interventions (NPIs), such as sanitary measures and travel restrictions, aimed at controlling the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), may affect the transmission dynamics of human respiratory syncytial virus (HRSV). We aimed to quantify the contribution of the sales of hand hygiene products and the number of international and domestic airline passenger arrivals on HRSV epidemic in Japan. Methods The monthly number of HRSV cases per sentinel site (HRSV activity) in 2020 was compared with the average of the corresponding period in the previous 6 years (from January 2014 to December 2020) using a monthly paired t-test. A generalized linear Poisson regression model was used to regress the time-series of the monthly HRSV activity against NPI indicators, including sale of hand hygiene products and the number of domestic and international airline passengers, while controlling for meteorological conditions (monthly average temperature and relative humidity) and seasonal variations between years (2014–2020). Results The average number of monthly HRSV case notifications in 2020 decreased by approximately 85% (P < 0.001) compared to those in the preceding 6 years (2014–2019) (Figure 1A). For every average ¥1 billion (approximately &9,000,000/£6,800,00) spent on hand hygiene products during the current month and 1 month before (lag 0-1 months) there was a 0.22% (P = 0.02) decrease in HRSV infections (Table 1). An increase of average 1,000 domestic and international airline passenger arrivals during the previous 1–2 months (lag 1–2 months) was associated with a 4.6×10−4% (P < 0.001) and 1.1×10−3% (P = 0.007) increase in the monthly number of HRSV infections, respectively. Figure 1. Monthly seasonal variations of number of HRSV activity, NPI indicators, and meteorological conditions during 2014-2020. (A) Monthly seasonal variations of number of HRSV cases per sentinel sites based on national HRSV surveillance data during 2014-2020. (B) Monthly seasonal variations of retail sales of hand hygiene products per ¥1 billion (unit: yen) during 2014-2020. (C) Monthly seasonal variations of number of domestic airline passengers per 1,000 population (unit: person) during 2014-2020. (D) Monthly seasonal variations of number of international airline passengers per 1,000 population (unit: person) during 2014-2020. (E) Monthly seasonal variations of average temperature (unit: ℃) throughout Japan during 2014-2020. (F) Monthly seasonal variations of relative humidity (unit: %) throughout Japan during 2014-2020. Table 1. Generalized linear Poisson regression model for the monthly number of human respiratory syncytial virus cases among prefectures in Japan. Conclusion This study suggests that there is an association between the decrease in the monthly number of HRSV cases and improved hygiene and sanitary measures and travel restrictions for COVID-19 in Japan, indicating that these public health interventions can contribute to the suppression of HRSV activity. These findings may help in public health policy and decision making. Disclosures All Authors: No reported disclosures

Background Statistical models are regularly used in the forecasting and surveillance of infectious diseases to guide public health. Variable selection assists in determining factors associated with disease transmission, however, often overlooked in this process is the evaluation and suitability of the statistical model used in forecasting disease transmission and outbreaks. Here we aim to evaluate several modelling methods to optimise predictive modelling of Ross River virus (RRV) disease notifications and outbreaks in epidemiological important regions of Victoria and Western Australia. Methodology/Principal findings We developed several statistical methods using meteorological and RRV surveillance data from July 2000 until June 2018 in Victoria and from July 1991 until June 2018 in Western Australia. Models were developed for 11 Local Government Areas (LGAs) in Victoria and seven LGAs in Western Australia. We found generalised additive models and generalised boosted regression models, and generalised additive models and negative binomial models to be the best fit models when predicting RRV outbreaks and notifications, respectively. No association was found with a model’s ability to predict RRV notifications in LGAs with greater RRV activity, or for outbreak predictions to have a higher accuracy in LGAs with greater RRV notifications. Moreover, we assessed the use of factor analysis to generate independent variables used in predictive modelling. In the majority of LGAs, this method did not result in better model predictive performance. Conclusions/Significance We demonstrate that models which are developed and used for predicting disease notifications may not be suitable for predicting disease outbreaks, or vice versa. Furthermore, poor predictive performance in modelling disease transmissions may be the result of inappropriate model selection methods. Our findings provide approaches and methods to facilitate the selection of the best fit statistical model for predicting mosquito-borne disease notifications and outbreaks used for disease surveillance., Author summary Mosquito-borne diseases cause significant illness worldwide. Mosquito breeding, which leads to disease transmission, is driven by favorable climatic and meteorological events (e.g., rainfall and warm temperatures). Understanding the association meteorological conditions have with mosquito breeding aids in directing mosquito control activities when there is a likelihood of disease transmission. Predictive models are used in public health decision making and resource allocation to guide mosquito control programs. However, there are multiple modelling methods, all of which provide differing degrees of accuracy in their predictions and suitability to the disease transmission dynamics. This study aims to assess commonly used statistical models for predicting mosquito-borne disease notifications and outbreaks. We demonstrate that statistical model selection plays an important role in accurately forecasting mosquito-borne disease and poor predictive performance may be due to inappropriate model selection. Furthermore, a model suited to predicting disease notifications may not always be the best model to accurately predict the occurrence of disease outbreaks. The methods used here can aid in public health to establish suitable predictive mosquito-borne disease surveillance systems to help guide disease prevention and resource allocation, and mosquito control activities.

Few studies have examined the effects of inbound overseas travelers and meteorological conditions on the shift in human respiratory syncytial virus (HRSV) season in Japan. This study aims to test whether the number of inbound overseas travelers and meteorological conditions are associated with the onset week of HRSV epidemic season. The estimation of onset week for 46 prefectures (except for Okinawa prefecture) in Japan for 4-year period (2014–2017) was obtained from previous papers based on the national surveillance data. We obtained data on the yearly number of inbound overseas travelers and meteorological (yearly mean temperature and relative humidity) conditions from Japan National Tourism Organization (JNTO) and Japan Meteorological Agency (JMA), respectively. Multi-level mixed-effects linear regression analysis showed that every 1 person (per 100,000 population) increase in number of overall inbound overseas travelers led to an earlier onset week of HRSV epidemic season in the year by 0.02 week (coefficient –0.02; P

Campus community gardens (CCGs) can potentially improve student health and wellbeing, mitigate social and ecological problems, and nurture university-community relationships. However, CCGs are located in complex socio-political and ecological settings and many community gardens struggle or fail. However, few studies have assessed the socio-political/ecological context of a garden setting prior to its development to understand the potential barriers and enablers of success. Our study assessed the socio-spatial context of a proposed CCG at a student university accommodation site. We engaged diverse university and community stakeholders through interviews, focus groups and a survey to explore their perceptions of the space generally and the proposed garden specifically. Visual observations and public life surveying were used to determine patterns of behavior. Results confirmed known problems associated with an underutilized site that provides little opportunity for lingering or contact with nature, and unknown barriers, including socially disconnected stakeholders and community distrust of the university. The research also uncovered positive enablers, such as stakeholder appreciation of the social, wellbeing and ecological benefits that a CCG could deliver. Our findings suggest that an in-depth exploration of a proposed garden context can be an important enabler of its success.

SUMMARYHealth warnings of mosquito-borne disease risk require forecasts that are accurate at fine-temporal resolutions (weekly scales); however, most forecasting is coarse (monthly). We use environmental and Ross River virus (RRV) surveillance to predict weekly outbreak probabilities and incidence spanning tropical, semi-arid, and Mediterranean regions of Western Australia (1991–2014). Hurdle and linear models were used to predict outbreak probabilities and incidence respectively, using time-lagged environmental variables. Forecast accuracy was assessed by model fit and cross-validation. Residual RRV notification data were also examined against mitigation expenditure for one site, Mandurah 2007–2014. Models were predictive of RRV activity, except at one site (Capel). Minimum temperature was an important predictor of RRV outbreaks and incidence at all predicted sites. Precipitation was more likely to cause outbreaks and greater incidence among tropical and semi-arid sites. While variable, mitigation expenditure coincided positively with increased RRV incidence (r2 = 0·21). Our research demonstrates capacity to accurately predict mosquito-borne disease outbreaks and incidence at fine-temporal resolutions. We apply our findings, developing a user-friendly tool enabling managers to easily adopt this research to forecast region-specific RRV outbreaks and incidence. Approaches here may be of value to fine-scale forecasting of RRV in other areas of Australia, and other mosquito-borne diseases.

Ross River virus (RRV) is Australia’s most epidemiologically important mosquito-borne disease. During RRV epidemics in the State of Victoria (such as 2010/11 and 2016/17) notifications can account for up to 30% of national RRV notifications. However, little is known about factors which can forecast RRV transmission in Victoria. We aimed to understand factors associated with RRV transmission in epidemiologically important regions of Victoria and establish an early warning forecast system. We developed negative binomial regression models to forecast human RRV notifications across 11 Local Government Areas (LGAs) using climatic, environmental, and oceanographic variables. Data were collected from July 2008 to June 2018. Data from July 2008 to June 2012 were used as a training data set, while July 2012 to June 2018 were used as a testing data set. Evapotranspiration and precipitation were found to be common factors for forecasting RRV notifications across sites. Several site-specific factors were also important in forecasting RRV notifications which varied between LGA. From the 11 LGAs examined, nine experienced an outbreak in 2011/12 of which the models for these sites were a good fit. All 11 LGAs experienced an outbreak in 2016/17, however only six LGAs could predict the outbreak using the same model. We document similarities and differences in factors useful for forecasting RRV notifications across Victoria and demonstrate that readily available and inexpensive climate and environmental data can be used to predict epidemic periods in some areas. Furthermore, we highlight in certain regions the complexity of RRV transmission where additional epidemiological information is needed to accurately predict RRV activity. Our findings have been applied to produce a Ross River virus Outbreak Surveillance System (ROSS) to aid in public health decision making in Victoria. Keywords: Arboviruses, Transmission, Mosquito-borne disease, Forecasting, Predicting epidemics, Epidemiology

Asthma and allergic rhinitis (or hay fever) are ubiquitous, chronic health conditions that seasonally affect a sizeable proportion of the population. Both are commonly triggered or exacerbated by environmental conditions including aeroallergens, air quality and weather. Smartphone technology offers new opportunities to identify environmental drivers by allowing large-scale, real-time collection of day-to-day symptoms. As yet, however, few studies have explored the potential of this technology to provide useful epidemiological data on environment-symptom relationships. Here, we use data from the smartphone app 'AirRater' to examine relationships between asthma and allergic rhinitis symptoms and weather, air quality and pollen loads in Hobart, Tasmania, Australia. We draw on symptom data logged by app users over a three-year period and use time-series analysis to assess the relationship between symptoms and environmental co-variates. Symptoms are associated with particulate matter (IRR 1.06, 95% CI: 1.04-1.08), maximum temperature (IRR 1.28, 95% CI: 1.13-1.44) and pollen taxa including Betula (IRR 1.04, 95% CI: 1.02-1.07), Cupressaceae (IRR 1.02, 95% CI: 1.01-1.04), Myrtaceae (IRR 1.06, 95% CI: 1.02-1.10) and Poaceae (IRR 1.05, 95% CI: 1.01-1.09). The importance of these pollen taxa varies seasonally and more taxa are associated with allergic rhinitis (eye/nose) than asthma (lung) symptoms. Our results are congruent with established epidemiological evidence, while providing important local insights including the association between symptoms and Myrtaceae pollen. We conclude that smartphone-sourced data can be a useful tool in environmental epidemiology.

Summary: We find that the spatial scale/aggregation of an analysis influences the apparent importance of ecological drivers of arboviral (Ross River virus) disease; we urge future epidemiological studies to include multiple spatial scales for a more complete picture of disease drivers. Background: Zoonotic vector-borne disease prevalence is affected by vector, human and reservoir host factors, which are influenced by habitat and climate; these five components interact on microhabitat to landscape scales but are often analyzed at a single spatial scale. Methods: We present an information theoretic, multi-scale, multiple regression analysis of the ecological drivers of Ross River virus. We analyze the spatial pattern of 20 years of Ross River virus infections from South Australia (1992-2012; n = 5,261) using variables across these five components of disease ecology at three spatial scales. Results: We found that covariate importance depended on the spatial scale of the analysis; some biotic variables were more important at fine scales and some abiotic variables were more important at coarser spatial scales. The urban score of an area was most predictive of infections and mosquito variables did not improve the explanatory power of these models. Conclusions: Through this multi-scale analysis, we identified novel drivers of the spatial distribution of disease and recommend public health interventions. Our results underline that single-scale analyses may paint an incomplete picture of disease drivers, potentially creating a major flaw in epidemiological analyses. Multi-scale, ecological analyses are needed to better understand infectious disease transmission.

Stressful conditions experienced during early development can have deleterious effects on offspring morphology, physiology and behaviour. However, few studies have examined how developmental stress influences an individual's cognitive phenotype. Using a viviparous lizard, we show that the availability of food resources to a mother during gestation influences a key component of her offspring's cognitive phenotype: their decision-making. Offspring from females who experienced low resource availability during gestation did better in an anti-predatory task that relied on spatial associations to guide their decisions, whereas offspring from females who experienced high resource availability during gestation did better in a foraging task that relied on colour associations to inform their decisions. This shows that the prenatal environment can influence decision-making in animals, a cognitive trait with functional implications later in life.

SUMMARYMost vector-borne diseases infect multiple host species, but disentangling the relative importance of different host species to transmission can be complex. Here we study how host species’ abundance and competence (duration and titre of parasitaemia) influence host importance during epidemic scenarios. We evaluate this theory using Ross River virus (RRV, family Togaviridae, genus Alphavirus), a multi-host mosquito-borne disease with significant human health impacts across Australia and Papua New Guinea. We used host contribution models to find the importance of key hosts (possums, wallabies, kangaroos, horses, humans) in typical mammal communities around five Australian epidemic centres. We found humans and possums contributed most to epidemic RRV transmission, owing to their high abundances, generally followed by macropods. This supports humans as spillover hosts, and that human–mosquito and possum–mosquito transmission is predominant during epidemics. Sensitivity analyses indicate these findings to be robust across epidemic centres. We emphasize the importance of considering abundance and competence in identifying key hosts (during epidemics in this case), and that competence alone is inadequate. Knowledge of host importance in disease transmission may help to equip health agencies to bring about greater effectiveness of disease mitigation strategies.

The purpose of this study was to use a novel smartphone based application, AirRater, to identify the most important local environmental factors associated with symptoms in people with allergic rhinitis and asthma, in Tasmania, Australia.