The El Nino-Southern Oscillation (ENSO)-pandemic influenza connection: coincident or causal?

How do El Niño Southern Oscillation (ENSO) and local meteorological factors affect the incidence of seasonal influenza in New York state

Background

Research is lacking in examining how multiple climate factors affect the incidence of seasonal influenza. We investigated the associations between El Niño Southern Oscillation (ENSO), meteorological factors, and influenza incidence in New York State, United States.

Method

We collected emergency department visit data for influenza from the New York State Department of Health. ENSO index was obtained from the National Oceanic and Atmospheric Administration. Meteorological factors, Google Flu Search Index (GFI), and Influenza-like illness (ILI) data in New York State were also collected. Wavelet analysis was used to quantitatively estimate the coherence and phase difference of ENSO, temperature, precipitation, relative humidity, and absolute humidity with emergency department visits of influenza in New York State. Generalized additive models (GAM) were employed to examine the exposure-response relationships between ENSO, weather, and influenza. GFI and ILI data were used to simulate synchronous influenza visits.

Results

The influenza epidemic in New York State had multiple periodic and was primarily on the 1-year scale. The incidence of influenza closely followed the low ENSO index by an average of two months, and the lag period of ENSO on influenza was shorter during 2015-2018. Low temperature in the previous 2 weeks and low absolute humidity in the prior week were positively associated with influenza incidence in New York State. We found an l-shaped association between ENSO index and influenza, a parabolic relationship between temperature in the previous two weeks and influenza, and a linear negative association between absolute humidity in the previous week and influenza. The simulation models including GFI and ILI had higher accuracy for influenza visit estimation.

Conclusions

Low ENSO index, low temperature, and low absolute humidity may drive the influenza epidemics in New York State. The findings can help us deepen the understanding of the climate-influenza association, and help to develop an influenza forecasting model.

Routing algorithms as tools for integrating social distancing with emergency evacuation

One of the lessons from the COVID-19 pandemic is the importance of social distancing, even in challenging circumstances such as pre-hurricane evacuation. To explore the implications of integrating social distancing with evacuation operations, we describe this evacuation process as a Capacitated Vehicle Routing Problem (CVRP) and solve it using a DNN (Deep Neural Network)-based solution (Deep Reinforcement Learning) and a non-DNN solution (Sweep Algorithm). A central question is whether Deep Reinforcement Learning provides sufficient extra routing efficiency to accommodate increased social distancing in a time-constrained evacuation operation. We found that, in comparison to the Sweep Algorithm, Deep Reinforcement Learning can provide decision-makers with more efficient routing. However, the evacuation time saved by Deep Reinforcement Learning does not come close to compensating for the extra time required for social distancing, and its advantage disappears as the emergency vehicle capacity approaches the number of people per household.

Impact of climatic, demographic and disease control factors on the transmission dynamics of COVID-19 in large cities worldwide

Approximately a year into the COVID-19 pandemic caused by the SARS-CoV-2 virus, many countries have seen additional "waves" of infections, especially in the temperate northern hemisphere. Other vulnerable regions, such as South Africa and several parts of South America have also seen cases rise, further impacting local economies and livelihoods. Despite substantial research efforts to date, it remains unresolved as to whether COVID-19 transmission has the same sensitivity to climate observed for other common respiratory viruses such as seasonal influenza. Here, we look for empirical evidence of seasonality using a robust estimation framework. For 359 large cities across the world, we estimated the basic reproduction number (R₀) using logistic growth curves fitted to cumulative case data. We then assess evidence for association with climatic variables through ordinary least squares (OLS) regression. We find evidence of seasonality, with lower R₀ within cities experiencing greater surface radiation (coefficient = -0.005, p < 0.001), after adjusting for city-level variation in demographic and disease control factors. Additionally, we find association between R₀ and temperature during the early phase of the epidemic in China. However, climatic variables had much weaker explanatory power compared to socioeconomic and disease control factors. Rates of transmission and health burden of the continuing pandemic will be ultimately determined by population factors and disease control policies.

Impact of climatic, demographic and disease control factors on the transmission dynamics of COVID-19 in large cities worldwide

Approximately a year into the COVID-19 pandemic caused by the SARS-CoV-2 virus, many countries have seen additional "waves" of infections, especially in the temperate northern hemisphere. Other vulnerable regions, such as South Africa and several parts of South America have also seen cases rise, further impacting local economies and livelihoods. Despite substantial research efforts to date, it remains unresolved as to whether COVID-19 transmission has the same sensitivity to climate observed for other common respiratory viruses such as seasonal influenza. Here, we look for empirical evidence of seasonality using a robust estimation framework. For 359 large cities across the world, we estimated the basic reproduction number (R0) using logistic growth curves fitted to cumulative case data. We then assess evidence for association with climatic variables through ordinary least squares (OLS) regression. We find evidence of seasonality, with lower R0 within cities experiencing greater surface radiation (coefficient = -0.005, p < 0.001), after adjusting for city-level variation in demographic and disease control factors. Additionally, we find association between R0 and temperature during the early phase of the epidemic in China. However, climatic variables had much weaker explanatory power compared to socioeconomic and disease control factors. Rates of transmission and health burden of the continuing pandemic will be ultimately determined by population factors and disease control policies.

A Spatiotemporal Tool to Project Hospital Critical Care Capacity and Mortality From COVID-19 in US Counties

Objectives. To create a tool to rapidly determine where pandemic demand for critical care overwhelms county-level surge capacity and to compare public health and medical responses.Methods. In March 2020, COVID-19 cases requiring critical care were estimated using an adaptive metapopulation SEIR (susceptible‒exposed‒infectious‒recovered) model for all 3142 US counties for future 21-day and 42-day periods from April 2, 2020, to May 13, 2020, in 4 reactive patterns of contact reduction-0%, 20%, 30%, and 40%-and 4 surge response scenarios-very low, low, medium, and high.Results. In areas with increased demand, surge response measures could avert 104 120 additional deaths-55% through high clearance of critical care beds and 45% through measures such as greater ventilator access. The percentages of lives saved from high levels of contact reduction were 1.9 to 4.2 times greater than high levels of hospital surge response. Differences in projected versus actual COVID-19 demands were reasonably small over time.Conclusions. Nonpharmaceutical public health interventions had greater impact in minimizing preventable deaths during the pandemic than did hospital critical care surge response. Ready-to-go spatiotemporal supply and demand data visualization and analytics tools should be advanced for future preparedness and all-hazards disaster response.

COVID-Clarity demands unification of health and environmental policy

Spillover of novel pathogens from wildlife to people, such as the virus responsible for the COVID-19 pandemic, is increasing and this trend is most strongly associated with tropical deforestation driven by agricultural expansion. This same process is eroding natural capital, reducing forest-associated health co-benefits, and accelerating climate change. Protecting and promoting tropical forests is one of the most immediate steps we can take to simultaneously mitigate climate change while reducing the risk of future pandemics; however, success in this undertaking will require greater connectivity of policy initiatives from local to global, as well as unification of health and environmental policy.

Influenza virus-flow from insects to humans as causative for influenza seasonality

Virus biomass outweighs human biomass, and insects biomass outweighs human biomass. Insects are regularly habited by viruses as well as humans, humans are further inhabited via insects. A model of viral flow is described and specified to explain influenza virus seasonality, which, in temperate climate, usually evolves when insects have mostly disappeared. With this hypothesis a coherent description of regular seasonal influenza and other seasonal respiratory virus infections in temperate climates is possible. The incidence of influenza under different circumstances e.g. temperature, humidity, or tropical conditions and different aspects like synchronicity of infections or in respect to evolutionary conditions do sustain this hypothesis if the behaviour of insects is considered.

The NSERC Canadian Lake Pulse Network: A national assessment of lake health providing science for water management in a changing climate

The distribution and quality of water resources vary dramatically across Canada, and human impacts such as land-use and climate changes are exacerbating uncertainties in water supply and security. At the national level, Canada has no enforceable standards for safe drinking water and no comprehensive water-monitoring program to provide detailed, timely reporting on the state of water resources. To provide Canada's first national assessment of lake health, the NSERC Canadian Lake Pulse Network was launched in 2016 as an academic-government research partnership. LakePulse uses traditional approaches for limnological monitoring as well as state-of-the-art methods in the fields of genomics, emerging contaminants, greenhouse gases, invasive pathogens, paleolimnology, spatial modelling, statistical analysis, and remote sensing. A coordinated sampling program of about 680 lakes together with historical archives and a geomatics analysis of over 80,000 lake watersheds are used to examine the extent to which lakes are being altered now and in the future, and how this impacts aquatic ecosystem services of societal importance. Herein we review the network context, objectives and methods.

Effects of pollution, low temperature and influenza syndrome on the excess mortality risk in winter 2016-2017

BACKGROUND

In the winter of 2016-2017, the number of deaths recorded in the north-west Europe was significantly higher than that in previous years. This spike in mortality was attributed principally to an influenza epidemic, but the contribution of air pollution and cold temperature has not been investigated. Information on the combined effect of low temperatures, influenza epidemic, and air pollution on mortality is inadequate. The objective of this study was to estimate the excess mortality in the winter of 2016-2017 in the metropolitan area of Milan, and to evaluate the independent short-term effect of 3 risk factors: low temperatures, the influenza epidemic, and air pollution.

METHODS

We used a case-crossover, time-stratified study design. Mortality data were collected on all people aged > 65 years who died of natural causes, due to respiratory diseases or cardiovascular diseases, between December 1, 2016 and February 15, 2017. Environmental data were extracted from the Regional Environmental Protection Agency. The National Surveillance Network provided data on influenza epidemic.

RESULTS

Among the 7590 natural deaths in people aged > 65 years, 965 (13%) were caused by respiratory conditions, and 2688 (35%) were caused by cardiovascular conditions. There were statistically significant associations between the minimum recorded temperature and deaths due to natural causes (OR = 0.966, 95% CI: 0.944-0.989), and cardiovascular conditions (OR = 0.961, 95% CI: 0.925-0.999). There were also statistically significant association between the influenza epidemic and deaths due to natural causes (OR = 1.198, 95% CI: 1.156-1.241), cardiovascular conditions (OR = 1.153, 95% CI: 1.088-1.223), and respiratory conditions (OR = 1.303, 95% CI: 1.166-1.456). High levels of PM10 (60 and 70 μg/m³) were associated with a statistically significant increase in natural and cause-specific mortality. There were statistically significant interactions between PM10 and influenza for cardiovascular-related mortality, and between influenza and temperature for deaths due to natural causes.

CONCLUSIONS

Excess of mortality in Milan during winter 2016-2017 was associated with influenza epidemic and concomitant environmental exposures, specifically, the combined effect of air pollution and low temperatures. Policies mitigating the effects of environmental risk factors should be implemented to prevent future excess mortality.

Scientists' warning to humanity: microorganisms and climate change

In the Anthropocene, in which we now live, climate change is impacting most life on Earth. Microorganisms support the existence of all higher trophic life forms. To understand how humans and other life forms on Earth (including those we are yet to discover) can withstand anthropogenic climate change, it is vital to incorporate knowledge of the microbial 'unseen majority'. We must learn not just how microorganisms affect climate change (including production and consumption of greenhouse gases) but also how they will be affected by climate change and other human activities. This Consensus Statement documents the central role and global importance of microorganisms in climate change biology. It also puts humanity on notice that the impact of climate change will depend heavily on responses of microorganisms, which are essential for achieving an environmentally sustainable future.

Waterborne Diseases Arising From Climate Change

As a result of increased frequency and intensity of heat waves, increased floods and droughts, change in climate will affect biological, physical, and chemical components of water through different paths thus enhancing the risk of waterborne diseases. Identifying the role of weather in waterborne infection is a priority public health research issue as climate change is predicted to increase the frequency of extreme precipitation and temperature events. This chapter provides evidence that precipitation and temperature can affect directly or indirectly water quality and consequently affect the health human. This chapter also highlights the complex relationship between precipitation or temperature and transmission of waterborne disease such as diarrheal disease, gastroenteritis, cryptosporidiosis, giardiasis, and cholera.

Spatiotemporal Patterns and Diffusion of the 1918 Influenza Pandemic in British India

The factors that drive spatial heterogeneity and diffusion of pandemic influenza remain debated. We characterized the spatiotemporal mortality patterns of the 1918 influenza pandemic in British India and studied the role of demographic factors, environmental variables, and mobility processes on the observed patterns of spread. Fever-related and all-cause excess mortality data across 206 districts in India from January 1916 to December 1920 were analyzed while controlling for variation in seasonality particular to India. Aspects of the 1918 autumn wave in India matched signature features of influenza pandemics, with high disease burden among young adults, (moderate) spatial heterogeneity in burden, and highly synchronized outbreaks across the country deviating from annual seasonality. Importantly, we found population density and rainfall explained the spatial variation in excess mortality, and long-distance travel via railroad was predictive of the observed spatial diffusion of disease. A spatiotemporal analysis of mortality patterns during the 1918 influenza pandemic in India was integrated in this study with data on underlying factors and processes to reveal transmission mechanisms in a large, intensely connected setting with significant climatic variability. The characterization of such heterogeneity during historical pandemics is crucial to prepare for future pandemics.

Back to the Future: Lessons Learned From the 1918 Influenza Pandemic

2018 marks the 100-year anniversary of the 1918 influenza pandemic, which killed ~50 million people worldwide. The severity of this pandemic resulted from a complex interplay between viral, host, and societal factors. Here, we review the viral, genetic and immune factors that contributed to the severity of the 1918 pandemic and discuss the implications for modern pandemic preparedness. We address unresolved questions of why the 1918 influenza H1N1 virus was more virulent than other influenza pandemics and why some people survived the 1918 pandemic and others succumbed to the infection. While current studies suggest that viral factors such as haemagglutinin and polymerase gene segments most likely contributed to a potent, dysregulated pro-inflammatory cytokine storm in victims of the pandemic, a shift in case-fatality for the 1918 pandemic toward young adults was most likely associated with the host's immune status. Lack of pre-existing virus-specific and/or cross-reactive antibodies and cellular immunity in children and young adults likely contributed to the high attack rate and rapid spread of the 1918 H1N1 virus. In contrast, lower mortality rate in in the older (>30 years) adult population points toward the beneficial effects of pre-existing cross-reactive immunity. In addition to the role of humoral and cellular immunity, there is a growing body of evidence to suggest that individual genetic differences, especially involving single-nucleotide polymorphisms (SNPs), contribute to differences in the severity of influenza virus infections. Co-infections with bacterial pathogens, and possibly measles and malaria, co-morbidities, malnutrition or obesity are also known to affect the severity of influenza disease, and likely influenced 1918 H1N1 disease severity and outcomes. Additionally, we also discuss the new challenges, such as changing population demographics, antibiotic resistance and climate change, which we will face in the context of any future influenza virus pandemic. In the last decade there has been a dramatic increase in the number of severe influenza virus strains entering the human population from animal reservoirs (including highly pathogenic H7N9 and H5N1 viruses). An understanding of past influenza virus pandemics and the lessons that we have learnt from them has therefore never been more pertinent.

Heavy rainfall triggers increased nocturnal flight in desert populations of the Pacific black duck (Anas superciliosa)

Understanding of avian nocturnal flight comes mainly from northern hemisphere species in seasonal temperate ecosystems where nocturnal flight is often precisely timed and entrained by annual photoperiod. Here we investigate patterns of nocturnal flight in waterbirds of Australian desert ecosystems that fly considerable distances to find temporary water bodies formed from rainfall which is highly unpredictable seasonally and spatially, and when there is sufficient water, they then breed. How they perform these feats of navigation and physiology remain poorly known. Using GPS tracking of 38 satellite tagged Pacific black ducks (Anas superciliosa) in two contrasting ecosystems, before and after heavy rainfall we revealed a key role for facultative nocturnal flight in the movement ecology of this species. After large rainfall events, birds rapidly increased nocturnal flight activity in the arid aseasonal ecosystem, but not in the mesic seasonal one. Nocturnal flights occurred throughout the night in both ecosystems. Long range flights (>50 km in 2 hours) occurred almost exclusively at night; at night the distance flown was higher than during the day, birds visited more locations, and the locations were more widely dispersed. Our work reveals that heavy rainfall triggers increased nocturnal flight activity in desert populations of waterbirds.

Environmental indicators for human norovirus outbreaks

Norovirus is the most common cause of outbreaks of non-bacterial gastroenteritis in human. While the winter seasonality of norovirus outbreaks has been widely reported, the association between norovirus outbreak epidemics and environmental factors remains not fully understood. This literature review is intended to improve understanding of environmental factors governing norovirus outbreaks and how the factors affect norovirus transmission. To that end, a large number of studies (67) from countries around the world were critically reviewed and discussed. Results of the literature review show that temperature, humidity, and rainfall are the most important environmental variables governing the norovirus epidemic cycle. It was found that low temperature between -6.6 and 20 °C, relative humidity between 10 and 66 %, and rainfall from 1 day to 3 months before an outbreak are effective ranges of the environmental factors, which favor the prevalence of norovirus. Some other environmental factors might have an association with the cycle of norovirus epidemics. However, further investigations are needed to understand effects of the other factors on norovirus incidence. The findings of this literature review improve our understanding of the relationship between norovirus outbreaks and environmental factors and provide the direction for future research on norovirus outbreaks.

Climate change and infectious diseases

Global changes are major determinants for infectious diseases, although attributable, part of climate change remains debatable. Vector-borne diseases are prone to be impacted by global warming, although other factors may play a substantial role, evidenced by the dramatic decrease in malaria in the last decades in places where climate change has deep and significant effects. There is now evidence that in some areas of the world, e.g. Horn of Africa, warm El Niño Southern Oscillations (ENSO), which are observed in the South Pacific Ocean, are associated with higher risk of emergence of Rift Valley fever, cholera and malaria and during cold La Niña events, dengue fever, chikungunya and yellow fever. This has been observed for these and other diseases in other parts of the world. For example, seasonal influenza outbreaks have been more intense (i.e. higher number) and more severe (i.e. higher mortality) when concomitant with La Niña events. Since climate scientists have recently observed that climate change is tied to more frequent and more intense ENSO events, we may foresee increases in frequency and severity in emerging infectious diseases in the world.

Is sunspot activity a factor in influenza pandemics?

INTRODUCTION

The 2009 AH1N1 pandemic became a global health concern, although fortunately, its worst anticipated effects were not realised. While the origins of such outbreaks remain poorly understood, it is very important to identify the precipitating factors in their emergence so that future pandemics can be detected as quickly as possible. Methords: Descriptive epidemiology was used to analyse the association between influenza pandemics and possible pandemics and relative number of sunspots. Non-conditional logistic regression was performed to analyse the statistical association between sunspot extremes and influenza pandemics to within plus or minus 1 year.

RESULTS

Almost all recorded influenza/possible pandemics have occurred in time frames corresponding to sunspot extremes, or +/- 1 year within such extremes. These periods were identified as important risk factors in both possible and confirmed influenza pandemics (odds ratio: 3.87; 95% confidence interval: 1.08 to 13.85).

CONCLUSIONS

Extremes of sunspot activity to within plus or minus 1 year may precipitate influenza pandemics. Mechanisms of epidemic initiation and early spread are discussed including primary causation by externally derived viral variants (from space via cometary dust). Efforts to construct a comprehensive early warning system for potential influenza and other viral pandemics that include analysis of sunspot activity and stratospheric sampling for viral variants should be supported. Copyright © 2016 John Wiley & Sons, Ltd.

Pathogen exposure varies widely among sympatric populations of wild and domestic felids across the United States

Understanding how landscape, host, and pathogen traits contribute to disease exposure requires systematic evaluations of pathogens within and among host species and geographic regions. The relative importance of these attributes is critical for management of wildlife and mitigating domestic animal and human disease, particularly given rapid ecological changes, such as urbanization. We screened > 1000 samples from sympatric populations of puma (Puma concolor), bobcat (Lynx rufus), and domestic cat (Felis catus) across urban gradients in six sites, representing three regions, in North America for exposure to a representative suite of bacterial, protozoal, and viral pathogens (Bartonella sp., Toxoplasma gondii, feline herpesvirus-1, feline panleukopenea virus, feline calicivirus, and feline immunodeficiency virus). We evaluated prevalence within each species, and examined host trait and land cover determinants of exposure; providing an unprecedented analysis of factors relating to potential for infections in domesticated and wild felids. Prevalence differed among host species (highest for puma and lowest for domestic cat) and was greater for indirectly transmitted pathogens. Sex was inconsistently predictive of exposure to directly transmitted pathogens only, and age infrequently predictive of both direct and indirectly transmitted pathogens. Determinants of pathogen exposure were widely divergent between the wild felid species. For puma, suburban land use predicted increased exposure to Bartonella sp. in southern California, and FHV-1 exposure increased near urban edges in Florida. This may suggest interspecific transmission with domestic cats via flea vectors (California) and direct contact (Florida) around urban boundaries. Bobcats captured near urban areas had increased exposure to T. gondii in Florida, suggesting an urban source of prey Bobcats captured near urban areas in Colorado and Florida had higher FIV exposure, possibly suggesting increased intraspecific interactions through pile-up of home ranges. Beyond these regional and pathogen specific relationships, proximity to the wildland-urban interface did not generally increase the probability of disease exposure in wild or domestic felids, empha- sizing the importance of local ecological determinants. Indeed, pathogen exposure was often negatively associated with the wildland-urban interface for all felids. Our analyses suggest cross-species pathogen transmission events around this interface may be infrequent, but followed by self-sustaining propagation within the new host species. virus; puma (Puma concolor); Toxoplasma gondii; urbanization.

Seasonal Influenza Epidemics and El Niños

Seasonal influenza epidemics occur annually during the winter in the northern and southern hemispheres, but timing of peaks and severity vary seasonally. Low humidity, which enhances survival and transmission of influenza virus, is the major risk factor. Both El Niño and La Niña phases of El Niño-southern oscillation (ENSO), which determine inter-annual variation of precipitation, are putative risk factors. This study was done to determine if seasonality, timing of peak, and severity of influenza epidemics are coupled to phases of ENSO. Monthly time series of positive specimens for influenza viruses and of multivariate El Niño-Southern Oscillation Index from January 2000 to August 2015 were analyzed. Seasonality, wavelet spectra, and cross-wavelet spectra analyses were performed. Of 31 countries in the dataset, 21 were in the northern hemisphere and 10 in the southern hemisphere. The highest number of influenza cases occurred in January in the northern hemisphere, but in July in the southern hemisphere, p < 0.0001. Seasonal influenza epidemic was coupled to El Niño, while low occurrence was coupled to La Niña. The moderate La Niña of 2010-2011 was followed by weak seasonal influenza epidemic. The influenza pandemic of 2009-2010 followed the moderate El Niño of 2009-2010, which had three peaks. Spectrograms showed time-varying periodicities of 6-48 months for ENSO, 6-24 months for influenza in the northern hemisphere, and 6-12 months for influenza in the southern hemisphere. Cross spectrograms showed time-varying periodicities at 6-36 months for ENSO and influenza in both hemispheres, p < 0.0001. Phase plots showed that influenza time series lagged ENSO in both hemispheres. Severity of seasonal influenza increases during El Niño, but decreases during La Niña. Coupling of seasonality, timing, and severity of influenza epidemics to the strength and waveform of ENSO indicate that forecast models of El Niño should be integrated into surveillance programs for influenza epidemics.

Non-stationary dynamics of climate variability in synchronous influenza epidemics in Japan

Seasonal variation in the incidence of influenza is widely assumed. However, few studies have examined non-stationary relationships between global climate factors and influenza epidemics. We examined the monthly incidence of influenza in Fukuoka, Japan, from 2000 to 2012 using cross-wavelet coherency analysis to assess the patterns of associations between indices for the Indian Ocean Dipole (IOD) and El Niño Southern Oscillation (ENSO). The monthly incidence of influenza showed cycles of 1 year with the IOD and 2 years with ENSO indices (Multivariate, Niño 4, and Niño 3.4). These associations were non-stationary and appeared to have major influences on the synchrony of influenza epidemics. Our study provides quantitative evidence that non-stationary associations have major influences on synchrony between the monthly incidence of influenza and the dynamics of the IOD and ENSO. Our results call for the consideration of non-stationary patterns of association between influenza cases and climatic factors in early warning systems.

Real-time influenza forecasts during the 2012-2013 season

Recently, we developed a seasonal influenza prediction system that uses an advanced data assimilation technique and real-time estimates of influenza incidence to optimize and initialize a population-based mathematical model of influenza transmission dynamics. This system was used to generate and evaluate retrospective forecasts of influenza peak timing in New York City. Here we present weekly forecasts of seasonal influenza developed and run in real time for 108 cites in the United States during the recent 2012–2013 season. Reliable ensemble forecasts of influenza outbreak peak timing with leads of up to 9 weeks were produced. Forecast accuracy increased as the season progressed, and the forecasts significantly outperformed alternate, analog prediction methods. By Week 52, prior to peak for the majority of cities, 63% of all ensemble forecasts were accurate. To our knowledge, this is the first time predictions of seasonal influenza have been made in real time and with demonstrated accuracy.

Effect of non-stationary climate on infectious gastroenteritis transmission in Japan

Local weather factors are widely considered to influence the transmission of infectious gastroenteritis. Few studies, however, have examined the non-stationary relationships between global climatic factors and transmission of infectious gastroenteritis. We analyzed monthly data for cases of infectious gastroenteritis in Fukuoka, Japan from 2000 to 2012 using cross-wavelet coherency analysis to assess the pattern of associations between indices for the Indian Ocean Dipole (IOD) and El Niño Southern Oscillation (ENSO). Infectious gastroenteritis cases were non-stationary and significantly associated with the IOD and ENSO (Multivariate ENSO Index [MEI], Niño 1 + 2, Niño 3, Niño 4, and Niño 3.4) for a period of approximately 1 to 2 years. This association was non-stationary and appeared to have a major influence on the synchrony of infectious gastroenteritis transmission. Our results suggest that non-stationary patterns of association between global climate factors and incidence of infectious gastroenteritis should be considered when developing early warning systems for epidemics of infectious gastroenteritis.

Diverse inter-continental and host lineage reassortant avian influenza A viruses in pelagic seabirds

Avian influenza A viruses (AIVs) often infect waterfowl, gulls and shorebirds, but other bird groups including pelagic seabirds also serve as hosts. In this study, we analyzed 21 AIVs found in two distant breeding colonies of Common Murre (Uria aalge) in Newfoundland and Labrador, Canada, during 2011. Phylogenetic analyses and genotype assignments were performed for the 21 Common Murre viruses together with all Common and Thick-billed Murre (Uria lomvia) AIV sequences available in public sequence databases. All fully characterized viruses from the Common Murres in 2011 were H1N2 subtype, but the genome sequences revealed greater diversity and the viruses belonged to four distinct genotypes. The four genotypes shared most segments in common, but reassortment was observed for PB2 and M segments. This provided direct genetic data of AIV diversification through segment reassortment during an outbreak of AIV infection in high-density breeding colonies. Analysis of the total collection of available murre viruses revealed a diverse collection of subtypes and gene lineages with high similarity to those found in viruses from waterfowl and gulls, and there was no indication of murre-specific AIV gene lineages. Overall, the virus gene pool in murres was predominantly made up of AIV lineages associated with waterfowl, but also featured considerable gull lineage genes and inter-continental reassortments. In particular, all but one of the 21 Common Murre viruses from 2011 in Newfoundland contained 1 or 2 Eurasian segments and 16 contained 1 gull lineage segment. This mosaic nature of characterized murre AIV genomes might reflect an under-recognized role of these pelagic seabirds in virus transmission across space and between bird host taxa.

Climate variability, weather and enteric disease incidence in New Zealand: time series analysis

BACKGROUND

Evaluating the influence of climate variability on enteric disease incidence may improve our ability to predict how climate change may affect these diseases.

OBJECTIVES

To examine the associations between regional climate variability and enteric disease incidence in New Zealand.

METHODS

Associations between monthly climate and enteric diseases (campylobacteriosis, salmonellosis, cryptosporidiosis, giardiasis) were investigated using Seasonal Auto Regressive Integrated Moving Average (SARIMA) models.

RESULTS

No climatic factors were significantly associated with campylobacteriosis and giardiasis, with similar predictive power for univariate and multivariate models. Cryptosporidiosis was positively associated with average temperature of the previous month (β =  0.130, SE =  0.060, p <0.01) and inversely related to the Southern Oscillation Index (SOI) two months previously (β =  -0.008, SE =  0.004, p <0.05). By contrast, salmonellosis was positively associated with temperature (β  = 0.110, SE = 0.020, p<0.001) of the current month and SOI of the current (β  = 0.005, SE = 0.002, p<0.050) and previous month (β  = 0.005, SE = 0.002, p<0.05). Forecasting accuracy of the multivariate models for cryptosporidiosis and salmonellosis were significantly higher.

CONCLUSIONS

Although spatial heterogeneity in the observed patterns could not be assessed, these results suggest that temporally lagged relationships between climate variables and national communicable disease incidence data can contribute to disease prediction models and early warning systems.