determining-temperature-preference-of-mosquitoes-and-other-ectotherms

Determining Temperature Preference of Mosquitoes and Other Ectotherms

Most insects and other ectotherms have a relatively narrow optimal temperature window, and deviation from their optima can have significant effects on ...

University of Liverpool

UK Health Security Agency

Zika, a mosquito-borne viral disease that emerged in South America in 2015, was declared a Public Health Emergency of International Concern by the WHO in February of 2016. We developed a climate-driven R mathematical model for the transmission risk of Zika virus (ZIKV) that explicitly includes two key mosquito vector species: Aedes aegypti and Aedes albopictus The model was parameterized and calibrated using the most up to date information from the available literature. It was then driven by observed gridded temperature and rainfall datasets for the period 1950-2015. We find that the transmission risk in South America in 2015 was the highest since 1950. This maximum is related to favoring temperature conditions that caused the simulated biting rates to be largest and mosquito mortality rates and extrinsic incubation periods to be smallest in 2015. This event followed the suspected introduction of ZIKV in Brazil in 2013. The ZIKV outbreak in Latin America has very likely been fueled by the 2015-2016 El Niño climate phenomenon affecting the region. The highest transmission risk globally is in South America and tropical countries where Ae. aegypti is abundant. Transmission risk is strongly seasonal in temperate regions where Ae. albopictus is present, with significant risk of ZIKV transmission in the southeastern states of the United States, in southern China, and to a lesser extent, over southern Europe during the boreal summer season.

Global risk model for vector-borne transmission of Zika virus reveals the role of El Niño 2015.

Mosquito-borne Zika virus (ZIKV) transmission has almost exclusively been detected in the tropics despite the distributions of its primary vectors extending farther into temperate regions. Therefore, it is unknown whether ZIKV's range has reached a temperature-dependent limit, or if it can spread into temperate climates. Using field-collected mosquitoes for biological relevance, we found that two common temperate mosquito species,  and , were competent for ZIKV. We orally exposed mosquitoes to ZIKV and held them at between 17 and 31°C, estimated the time required for mosquitoes to become infectious, and applied these data to a ZIKV spatial risk model. We identified a minimum temperature threshold for the transmission of ZIKV by mosquitoes between 17 and 19°C. Using these data, we generated standardized basic reproduction number -based risk maps and we derived estimates for the length of the transmission season for recent and future climate conditions. Our standardized -based risk maps show potential risk of ZIKV transmission beyond the current observed range in southern USA, southern China and southern European countries. Transmission risk is simulated to increase over southern and Eastern Europe, northern USA and temperate regions of Asia (northern China, southern Japan) in future climate scenarios.

Potential for Zika virus transmission by mosquitoes in temperate climates.

Mutualistic interactions between species are ubiquitous in nature and essential for ecosystem functioning. Often dozens or even hundreds of species with different degrees of specialisation form complex networks. How this complexity evolves is a fundamental question in ecology. Here, we present a new game theoretical approach to model complex coevolutionary processes and apply it to pollination networks. A theoretical analysis reveals multiple evolutionary stable network structures that depend on the availability of pollination service. In particular, we find efficient communities, in which a high percentage of pollen are transported conspecifically, to evolve only when plant and pollinator abundances are well balanced. Both pollinator shortage and oversupply select for more inefficient network structures. The results suggest that availability of pollination services is a key factor structuring pollination networks and may offer a new explanation for geographical differences in pollination communities that have long been recognised by ecologists.

Evolutionary stability of plant-pollinator networks: efficient communities and a pollination dilemma.

Historically, Jiangxi province has had the largest HFRS burden in China. However, thus far, the comprehensive understanding of the spatiotemporal distributions of HFRS is limited in Jiangxi. In this study, seasonal decomposition analysis, spatial autocorrelation analysis, and space-time scan statistic analyses were performed to detect the spatiotemporal dynamics distribution of HFRS cases from 2005 to 2018 in Jiangxi at the county scale. The epidemic of HFRS showed the characteristic of bi-peak seasonality, the primary peak in winter (November to January) and the second peak in early summer (May to June), and the amplitude and the magnitude of HFRS outbreaks have been increasing. The results of global and local spatial autocorrelation analysis showed that the HFRS epidemic exhibited the characteristic of highly spatially heterogeneous, and Anyi, Fengxin, Yifeng, Shanggao, Jing'an and Gao'an county were hot spots areas. A most likely cluster, and two secondary likely clusters were detected in 14-years duration. The higher risk areas of the HFRS outbreak were mainly located in Jiangxi northern hilly state, spreading to Wuyi mountain hilly state as time advanced. This study provided valuable information for local public health authorities to design and implement effective measures for the control and prevention of HFRS.

Spatiotemporal dynamics of hemorrhagic fever with renal syndrome in Jiangxi province, China.

To develop and validate a pragmatic risk score to predict mortality in patients admitted to hospital with coronavirus disease 2019 (covid-19).

Risk stratification of patients admitted to hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol: development and validation of the 4C Mortality Score.

BackgroundThe progression and geographical distribution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in the United Kingdom (UK) and elsewhere is unknown because typically only symptomatic individuals are diagnosed. We performed a serological study of blood donors in Scotland in the spring of 2020 to detect neutralising antibodies to SARS-CoV-2 as a marker of past infection and epidemic progression.AimOur objective was to determine if sera from blood bank donors can be used to track the emergence and progression of the SARS-CoV-2 epidemic.MethodsA pseudotyped SARS-CoV-2 virus microneutralisation assay was used to detect neutralising antibodies to SARS-CoV-2. The study comprised samples from 3,500 blood donors collected in Scotland between 17 March and 18 May 2020. Controls were collected from 100 donors in Scotland during 2019.ResultsAll samples collected on 17 March 2020 (n = 500) were negative in the pseudotyped SARS-CoV-2 virus microneutralisation assay. Neutralising antibodies were detected in six of 500 donors from 23 to 26 March. The number of samples containing neutralising antibodies did not significantly rise after 5-6 April until the end of the study on 18 May. We found that infections were concentrated in certain postcodes, indicating that outbreaks of infection were extremely localised. In contrast, other areas remained comparatively untouched by the epidemic.ConclusionAlthough blood donors are not representative of the overall population, we demonstrated that serosurveys of blood banks can serve as a useful tool for tracking the emergence and progression of an epidemic such as the SARS-CoV-2 outbreak.

Detection of neutralising antibodies to SARS-CoV-2 to determine population exposure in Scottish blood donors between March and May 2020.

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 (R) 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 R within cities experiencing greater surface radiation (coefficient = -0.005,  < 0.001), after adjusting for city-level variation in demographic and disease control factors. Additionally, we find association between R 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.

Dengue is considered non-endemic to mainland China. However, travellers frequently import the virus from overseas and local mosquito species can then spread the disease in the population. As a consequence, mainland China still experiences large dengue outbreaks. Temperature plays a key role in these outbreaks: it affects the development and survival of the vector and the replication rate of the virus. To better understand its implication in the transmission risk of dengue, we developed a delay differential equation model that explicitly simulates temperature-dependent development periods and tested it with collected field data for the Asian tiger mosquito, Aedes albopictus. The model predicts mosquito occurrence locations with a high accuracy (Cohen's κ of 0.78) and realistically replicates mosquito population dynamics. Analysing the infection dynamics during the 2014 dengue outbreak that occurred in Guangzhou showed that the outbreak could have lasted for another four weeks if mosquito control interventions had not been undertaken. Finally, we analyse the dengue transmission risk in mainland China. We find that southern China, including Guangzhou, can have more than seven months of dengue transmission per year while even Beijing, in the temperate north, can have dengue transmission during hot summer months. The results demonstrate the importance of using detailed vector and infection ecology, especially when vector-borne disease transmission risk is modelled over a broad range of climatic zones.

Soeren Metelmann

Department of Evolution,
Ecology,
and Behavior,
Institute of Infection,
Veterinary and Ecological Sciences,
Department of Evolution, Ecology, and Behavior, Institute of Infection, Veterinary and Ecological Sciences

University of Liverpool

Determining Temperature Preference of Mosquitoes and Other Ectotherms

Soeren Metelmann

.css-o250i3{font-size:18px;font-family:Open Sans,sans-serif;line-height:21.6px;}Department of Evolution,Ecology,and Behavior,Institute of Infection,Veterinary and Ecological Sciences,Department of Evolution, Ecology, and Behavior, Institute of Infection, Veterinary and Ecological Sciences

University of Liverpool

Determining Temperature Preference of Mosquitoes and Other Ectotherms

Department of Evolution,
Ecology,
and Behavior,
Institute of Infection,
Veterinary and Ecological Sciences,
Department of Evolution, Ecology, and Behavior, Institute of Infection, Veterinary and Ecological Sciences