The seasonal reproduction number of dengue fever: impacts of climate on transmission

Interepidemic xenosurveillance of Japanese encephalitis virus and Zika virus in Culex mosquitoes from Ubon Ratchathani province, Thailand

Background and Aim

Some Culex mosquitoes are competent vectors for Japanese encephalitis virus (JEV) and Zika virus (ZIKV), which cause public health problems worldwide, especially in South-east Asia. Xenosurveillance of Culex mosquitoes remains limited compared with other common mosquito-borne diseases. This study aimed to identify JEV and ZIKV in field-caught Culex mosquitoes collected from Ubon Ratchathani province.

Materials and Methods

We investigated the presence of JEV and ZIKV in Culex mosquitoes from two districts in Ubon Ratchathani province, Thailand, and examined their role in viral interepidemic circulation. Female Culex mosquitoes (5,587) were collected using a mechanical aspirator from indoors and outdoors. The consensus sequences of the E and NS1 genes of JEV and the E gene of ZIKV were identified using real-time reverse transcription-polymerase chain reaction.

Results

From 335 sample pools that contain a total of 5587 adult female Culex mosquitoes collected from Don Yung, Mueang district (4,406) and Phon Duan, Det Udom district (1,181), none of the collected mosquitoes tested positive for either JEV or ZIKV.

Conclusion

This study did not find JEV and ZIKV in Culex mosquitoes collected from the area of collection, which may be due to the low circulating amount of the virus in the vectors in the area, making it undetectable, or it may be because Culex mosquitoes are not suitable vector for the virus being tested. However, further xenosurveillance study of JEV and ZIKV in mosquito is suggested to prepare for the next outbreak.

Epidemiological and hematological investigation of Dengue Virus infection

Dengue is a mosquito-borne viral illness that infects humans. For the last few decades, it has been declared a global public health problem. The current study was conducted at the district headquarter hospital (DHQ) Bannu between June to September 2018 based on the seroprevalence of antibodies against dengue virus serotypes and their hematological parameters among the patients. A total of 1738 suspected individuals were diagnosed through NS1, IgG, and IgM antibodies and RT-PCR techniques. Out of all the samples, 716 (41.19%) were found to be positive for dengue. A higher infection rate was found in males (65.92%) as compared to females (34.07%). The most affected age group was 16-40 years, whereas the most affected tehsil was Bannu, where the DENV-3 serotype was prevalent. The rare serotype (DENV-4) was found in 1% of cases. Symptoms including fever (100%), myalgia (100%), headache (61.31%), vomiting (34.63%), and rashes were common among the dengue patients. However, the mild cases showed fewer clinical signs compared to the severe infected cases. The study also revealed a significant association (P<0.05) between hematological parameters and dengue infection, showing a significant decrease in TC, eosinophils, neutrophils, and platelets and a significant increase in monocytes and lymphocytes. Based on the current report, it is concluded that patients with the above symptoms and hematological changes may have increased probability of Dengue and should be kept under observation to separate dengue positive patients and enhance treatment process. This article is protected by copyright. All rights reserved.

A retrospective study of environmental predictors of dengue in Delhi from 2015 to 2018 using the generalized linear model

Dengue fever is a mosquito-borne infection with a rising trend, expected to increase further with the rise in global temperature. The study aimed to use the environmental and dengue data 2015–2018 to examine the seasonal variation and establish a probabilistic model of environmental predictors of dengue using the generalized linear model (GLM). In Delhi, dengue cases started emerging in the monsoon season, peaked in the post-monsoon, and thereafter, declined in early winter. The annual trend of dengue cases declined, but the seasonal pattern remained alike (2015–18). The Spearman correlation coefficient of dengue was significantly high with the maximum and minimum temperature at 2 months lag, but it was negatively correlated with the difference of average minimum and maximum temperature at lag 1 and 2. The GLM estimated β coefficients of environmental predictors such as temperature difference, cumulative rainfall, relative humidity and maximum temperature were significant (p < 0.01) at different lag (0 to 2), and maximum temperature at lag 2 was having the highest effect (IRR 1.198). The increasing temperature of two previous months and cumulative rainfall are the best predictors of dengue incidence. The vector control should be implemented at least 2 months ahead of disease transmission (August–November).

Prediction of dengue fever outbreaks using climate variability and Markov chain Monte Carlo techniques in a stochastic susceptible-infected-removed model

The recent increase in the global incidence of dengue fever resulted in over 2.7 million cases in Latin America and many cases in Southeast Asia and has warranted the development and application of early warning systems (EWS) for futuristic outbreak prediction. EWS pertaining to dengue outbreaks is imperative; given the fact that dengue is linked to environmental factors owing to its dominance in the tropics. Prediction is an integral part of EWS, which is dependent on several factors, in particular, climate, geography, and environmental factors. In this study, we explore the role of increased susceptibility to a DENV serotype and climate variability in developing novel predictive models by analyzing RT-PCR and DENV-IgM confirmed cases in Singapore and Honduras, which reported high dengue incidence in 2019 and 2020, respectively. A random-sampling-based susceptible-infected-removed (SIR) model was used to obtain estimates of the susceptible fraction for modeling the dengue epidemic, in addition to the Bayesian Markov Chain Monte Carlo (MCMC) technique that was used to fit the model to Singapore and Honduras case report data from 2012 to 2020. Regression techniques were used to implement climate variability in two methods: a climate-based model, based on individual climate variables, and a seasonal model, based on trigonometrically varying transmission rates. The seasonal model accounted for 98.5% and 92.8% of the variance in case count in the 2020 Singapore and 2019 Honduras outbreaks, respectively. The climate model accounted for 75.3% and 68.3% of the variance in Singapore and Honduras outbreaks respectively, besides accounting for 75.4% of the variance in the major 2013 Singapore outbreak, 71.5% of the variance in the 2019 Singapore outbreak, and over 70% of the variance in 2015 and 2016 Honduras outbreaks. The seasonal model accounted for 14.2% and 83.1% of the variance in the 2013 and 2019 Singapore outbreaks, respectively, in addition to 91% and 59.5% of the variance in the 2015 and 2016 Honduras outbreaks, respectively. Autocorrelation lag tests showed that the climate model exhibited better prediction dynamics for Singapore outbreaks during the dry season from May to August and in the rainy season from June to October in Honduras. After incorporation of susceptible fractions, the seasonal model exhibited higher accuracy in predicting outbreaks of higher case magnitude, including those of the 2019–2020 dengue epidemic, in comparison to the climate model, which was more accurate in outbreaks of smaller magnitude. Such modeling studies could be further performed in various outbreaks, such as the ongoing COVID-19 pandemic to understand the outbreak dynamics and predict the occurrence of future outbreaks.

Epidemic prediction of dengue fever based on vector compartment model and Markov chain Monte Carlo method

Background

Dengue epidemics is affected by vector-human interactive dynamics. Infectious disease prevention and control emphasize the timing intervention at the right diffusion phase. In such a way, control measures can be cost-effective, and epidemic incidents can be controlled before devastated consequence occurs. However, timing relations between a measurable signal and the onset of the pandemic are complex to be discovered, and the typical lag period regression is difficult to capture in these complex relations. This study investigates the dynamic diffusion pattern of the disease in terms of a probability distribution. We estimate the parameters of an epidemic compartment model with the cross-infection of patients and mosquitoes in various infection cycles. We comprehensively study the incorporated meteorological and mosquito factors that may affect the epidemic of dengue fever to predict dengue fever epidemics.

Results

We develop a dual-parameter estimation algorithm for a composite model of the partial differential equations for vector-susceptible-infectious-recovered with exogeneity compartment model, Markov chain Montel Carlo method, and boundary element method to evaluate the epidemic periodicity under the effect of environmental factors of dengue fever, given the time series data of 2000–2016 from three cities with a population of 4.7 million. The established computer model of “energy accumulation-delayed diffusion-epidemics” is proven to be effective to predict the future trend of reported and unreported infected incidents. Our artificial intelligent algorithm can inform the authority to cease the larvae at the highest vector infection time. We find that the estimated dengue report rate is about 20%, which is close to the number of official announcements, and the percentage of infected vectors increases exponentially yearly. We suggest that the executive authorities should seriously consider the accumulated effect among infected populations. This established epidemic prediction model of dengue fever can be used to simulate and evaluate the best time to prevent and control dengue fever.

Conclusions

Given our developed model, government epidemic prevention teams can apply this platform before they physically carry out the prevention work. The optimal suggestions from these models can be promptly accommodated when real-time data have been continuously corrected from clinics and related agents.

The Epidemic Risk of Dengue Fever in Japan: Climate Change and Seasonality

Dengue fever is a leading cause of illness and death in the tropics and subtropics, and the disease has become a threat to many nonendemic countries where the competent vectors such as Aedes albopictus and Aedes aegypti are abundant. The dengue epidemic in Tokyo, 2014, poses the critical importance to accurately model and predict the outbreak risk of dengue fever in nonendemic regions. Using climatological datasets and traveler volumes in Japan, where dengue was not seen for 70 years by 2014, we investigated the outbreak risk of dengue in 47 prefectures, employing the temperature-dependent basic reproduction number and a branching process model. Our results show that the effective reproduction number varies largely by season and by prefecture, and, moreover, the probability of outbreak if an untraced case is imported varies greatly with the calendar time of importation and location of destination. Combining the seasonally varying outbreak risk with time-dependent traveler volume data, the unconditional outbreak risk was calculated, illustrating different outbreak risks between southern coastal areas and northern tourist cities. As the main finding, the large travel volume with nonnegligible risk of outbreak explains the reason why a summer outbreak in Tokyo, 2014, was observed. Prefectures at high risk of future outbreak would be Tokyo again, Kanagawa or Osaka, and highly populated prefectures with large number of travelers.

Aedes mosquito surveillance and the use of a larvicide for vector control in a rural area of the Lao People's Democratic Republic

Background

Refillable water containers are commonly used in rural areas of Lao PDR, and they act as Aedes mosquito breeding sites. Aedes aegypti and Ae. albopictus mosquitos are transmission vectors for the dengue virus, which causes dengue fever.

Methods

Two isolated rural villages in the central part of Lao PDR were selected as study sites. In the intervention village, domestic water containers were continuously treated with a long-lasting matrix release formulation, containing pyriproxyfen, named SumiLarv®2MR. In the control village, entomological activity was monitored, but no intervention was performed. Baseline data were collected in both villages during the late rainy season (October 2017) then distributed SumiLarv®2MR disks in intervention village. This data was compared with data collected during the intervention periods in the dry season (February 2018), rainy season (July 2018 and 2019), and late rainy season (September 2018) in the region.

Results

Compared with the baseline data (20.24%), the percentage of water containers infested with Ae. aegypti larvae was significantly decreased in the treated village, especially in the rainy seasons in July 2018 (4.11%; P < 0.001) and July 2019 (2.46%; P < 0.001), while the percentage of water containers infested with Ae. albopictus larvae did not decrease significantly in prevalence. No reduction in the frequency of Aedes species was seen in the control village. The Ae. albopictus liked to breed in small habitats (the median water volume of its habitats was 5 L and 10 L in the control and treated village, respectively, while the equivalent values for Ae. aegypti were 30 L and 50 L, respectively).

Conclusion

The treatment of refillable water storage containers in a rural village with SumiLarv®2MR disks led to significant reductions in the Ae. aegypti population. However, the Ae. albopictus population did not decrease in either the control or treated village. This discrepancy was due to differences in habitat-seeking behaviors and preferred breeding sites such as types of water, water container, and water volume, then led to the differences in results of mosquito prevalence after SumiLarv®2MR disk treatments. The SumiLarv®2MR disk treatment was proven to be effective against the primary dengue-virus vector mosquitoes, Ae. aegypti.

Clinical Course and Management of Dengue in Children Admitted to Hospital: A 5 Years Prospective Cohort Study in Jakarta, Indonesia

BACKGROUND

Dengue incidence is rising globally which was estimated 100 million per year, whereas in Indonesia was estimated 7.5 million per year. Dengue clinical course varies from mild dengue fever (DF) to dengue hemorrhagic fever (DHF) or dengue shock syndrome (DSS). Patients, clinicians and care facilities would benefit if reliable predictors can determine at admission which cases with clinically suspected dengue will progress to DHF or DSS.

METHODS

From 2009 through 2013, a cohort of 494 children admitted with clinically suspected dengue at a tertiary care hospital in Jakarta, Indonesia, was followed until discharge. We evaluated the clinical course and disease outcome of admitted patients and estimated the burden of dengue cases hospitalized over time.

RESULTS

Of all 494 children, 185 (37%) were classified at admission as DF, 158 (32%) as DHF and 151 (31%) as DSS. Of DF patients, 52 (28%) progressed to DHF or DSS, 10 (5%) had other viral diseases. Of DHF patients, 9(6%) progressed to DSS. Of 33 routinely collected parameters at admission, duration of fever ≤4 days was the only significant predictor of disease progression (P = 0.01). Five cases (3%) admitted with DSS died. Between 2009 and 2013, annual dengue admissions declined, while distribution of disease severity remained stable.

CONCLUSIONS

Almost a third of children admitted to tertiary care with clinically suspected DF progress to DHF or DSS. Among routinely collected parameters at admission, only fever duration was significantly associated with clinical progression, emphasizing unpredictability of dengue disease course from parameters currently routinely collected.

Periods of high dengue transmission defined by rainfall do not impact efficacy of dengue vaccine in regions of endemic disease

OBJECTIVE

To evaluate the association of rainy season with overall dengue disease incidence and with the efficacy of the Sanofi Pasteur recombinant, live, attenuated, tetravalent vaccine (CYD-TDV) in two randomized, controlled multicenter phase III clinical trials in Asia and Latin America.

METHODS

Rainy seasons were defined for each study site using climatological information from the World Meteorological Organization. The dengue attack rate in the placebo group for each study month was calculated as the number of symptomatic, virologically-confirmed dengue events in a given month divided by the number of participants at risk in the same month. Time-dependent Cox proportional hazard models were used to test whether rainy season was associated with dengue disease and whether it modified vaccine efficacy in each of the two trials and in both of the trials combined.

FINDINGS

Rainy season, country, and age were all significantly associated with dengue disease in both studies. Vaccine efficacy did not change during the rainy season in any of the analyses.

CONCLUSIONS

Although dengue transmission and exposure are expected to increase during the rainy season, our results indicate that CYD-TDV vaccine efficacy remains constant throughout the year in endemic regions.

SIR Model for Dengue Disease with Effect of Dengue Vaccination

The dengue disease is caused by dengue virus, and there is no specific treatment. The medical care by experienced physicians and nurses will save life and will lower the mortality rate. A dengue vaccine to control the disease is available in Thailand since late 2016. A mathematical model would be an important way to analyze the effects of the vaccination on the transmission of the disease. We have formulated an SIR (susceptible-infected-recovered) model of the transmission of the disease which includes the effect of vaccination and used standard dynamical modelling methods to analyze the effects. The equilibrium states and their stabilities are investigated. The trajectories of the numerical solutions plotted into the 2D planes and 3D spaces are presented. The main contribution is determining the role of dengue vaccination in the model. From the analysis, we find that there is a significant reduction in the total hospitalization time needed to treat the illness.

A forecasting model for dengue incidence in the District of Gampaha, Sri Lanka

Background

Dengue is one of the major health problems in Sri Lanka causing an enormous social and economic burden to the country. An accurate early warning system can enhance the efficiency of preventive measures. The aim of the study was to develop and validate a simple accurate forecasting model for the District of Gampaha, Sri Lanka. Three time-series regression models were developed using monthly rainfall, rainy days, temperature, humidity, wind speed and retrospective dengue incidences over the period January 2012 to November 2015 for the District of Gampaha, Sri Lanka. Various lag times were analyzed to identify optimum forecasting periods including interactions of multiple lags. The models were validated using epidemiological data from December 2015 to November 2017. Prepared models were compared based on Akaike’s information criterion, Bayesian information criterion and residual analysis.

Results

The selected model forecasted correctly with mean absolute errors of 0.07 and 0.22, and root mean squared errors of 0.09 and 0.28, for training and validation periods, respectively. There were no dengue epidemics observed in the district during the training period and nine outbreaks occurred during the forecasting period. The proposed model captured five outbreaks and correctly rejected 14 within the testing period of 24 months. The Pierce skill score of the model was 0.49, with a receiver operating characteristic of 86% and 92% sensitivity.

Conclusions

The developed weather based forecasting model allows warnings of impending dengue outbreaks and epidemics in advance of one month with high accuracy. Depending upon climatic factors, the previous month’s dengue cases had a significant effect on the dengue incidences of the current month. The simple, precise and understandable forecasting model developed could be used to manage limited public health resources effectively for patient management, vector surveillance and intervention programmes in the district.

Electronic supplementary material

The online version of this article (10.1186/s13071-018-2828-2) contains supplementary material, which is available to authorized users.

THE “UNHOLY” CHIKUNGUNYA–DENGUE–ZIKA TRINITY: A THEORETICAL ANALYSIS

Aedes aegypti is the vector for numerous diseases in humans and other (reservoir) hosts, such as chikungunya, dengue fever and Zika virus. A new deterministic model is designed and used to assess the dynamics of the three diseases in a population where Aedes mosquitoes are abundant. The model to be designed incorporates the recently-released imperfect vaccine against dengue virus (Dengvaxia[Formula: see text] vaccine by Sanofi Pasteur) as well as allow for sexual transmission of Zika. Further, the model allows for the assessment of the population-level impact of three biological hypotheses, namely a competitive dengue–chikungunya–Zika superinfection hierarchy, an antibody-dependent enhancement of dengue over Zika and that the Dengvaxia vaccine can induce reduced susceptibility to Zika infection in vaccinated individuals. After carrying out detailed theoretical analyses to gain insight into its qualitative features, the model is then fitted to the data recorded during the 2015–2016 outbreaks of the three diseases in Mexico. Simulations of the model show a reasonable fit to observed dynamics consistent with the competitive hierarchy assumed for the interactions of the viruses. Furthermore, Zika transmission dynamics is only mildly affected by changes in the parameter related to the infectiousness of Zika in relation to dengue, even in the region where antibody-dependent enhancement is assumed. The dengue vaccine has a very marginal impact on Zika transmission dynamics (and that the vaccine, no matter the coverage and efficacy levels, is unable to reduce the reproduction number for Zika transmission to a value less than unity). The model is extended to include the effect of seasonality and local weather variability (temperature and rainfall) on the dynamics of the three diseases. Simulations of the resulting non-autonomous model, using weather and demographic data for Mexico, show that for the current mean monthly rainfall value for Mexico, the burden of the three diseases increases with increasing mean monthly temperature in the range 16–29[Formula: see text]C, and decreases with increasing mean monthly temperature thereafter. Additionally, for the current fixed mean monthly temperature and rainfall data for Mexico, simulations show maximum transmission activity of all three diseases if the temperature and rainfall values lie in the range 25–26.4[Formula: see text]C and 90–128[Formula: see text]mm, respectively (these values are typically recorded in Mexico during the months of June, July and September). Simulations for two Mexican states (Oaxaca and Chiapas) where the three diseases are endemic show maximum transmission activity for all three diseases when temperature and rainfall lie in the ranges 20–25[Formula: see text]C and 51–102[Formula: see text]mm for Oaxaca (these ranges are recorded during the months of May through September) and 19–21[Formula: see text]C and 85–107[Formula: see text]mm for Chiapas (there ranges are recorded during the months of May, July, August and October), respectively. These simulations suggest suitable time when anti-mosquito control efforts should be intensified in Mexico (and the two selected states).

Non-linear Dynamics of Two-Patch Model Incorporating Secondary Dengue Infection

In this paper, the impact of human migration on the dynamics of dengue epidemic has been discussed. The vector-host model considers two patches with different dengue serotype in each patch. The model considers the constant rate of migration in susceptible and recovered class from one patch to other. Recovered migrants from prior infection are exposed to secondary infection in the patch where different serotype is present. The basic reproduction number is computed and analyzed in terms of migration parameters. The model is analyzed for the existence and local stability of various equilibrium states in terms of migration parameters. The numerical simulations for the choice of relevant data from literature have been performed to verify analytical results and to further explore the dynamics of the system. The sensitivity analysis of basic reproduction number with respect to migration parameters is carried out. It is found that immigration in a patch increases the basic reproduction in respective patch and vice-versa. The basic reproduction number has been estimated for the two states of Brazil which verifies the occurrence of severe epidemic in one of the states of Brazil.

Analysis of Health Indicators in Two Rural Communities on the Colombian Caribbean Coast: Poor Water Supply and Education Level Are Associated with Water-Related Diseases

Water-related diseases are closely linked with drinking water, sanitation, and hygiene (WASH) indicators, socioeconomic status, education level, or dwelling's conditions. Developing countries exhibit a particular vulnerability to these diseases, especially rural areas and urban slums. This study assessed socioeconomic features, WASH indicators, and water-related diseases in two rural areas of the Colombian Caribbean coast. Most of this population did not finish basic education (72.3%, N = 159). Only one of the communities had a water supply (aqueduct), whereas the other received water via an adapted tanker ship. No respondents reported sewage services; 92.7% (N = 204) had garbage service. Reported cases of diarrhea were associated with low education levels (P = 2.37 × 10-9) and an unimproved drinking water supply (P = 0.035). At least one fever episode was reported in 20% (N = 44) of dwellings, but the cases were not related to any indicator. The Aedes/House index (percentage of houses that tested positive for Aedes larvae and/or pupae) was 69%, the container index (percentage of water-holding containers positive for Aedes larvae or pupae) 29.4%, and the Breteau index (number of positive containers per 100 houses in a specific location) was three positive containers per 100 inspected houses. The presence of positive containers was associated with the absence of a drinking water supply (P = 0.04). The community with poorer health indicators showed greater health vulnerability conditions for acquisition of water-related diseases. In summary, water supply and educational level were the main factors associated with the presence of water-related diseases in both communities.

Serological and spatial analysis of alphavirus and flavivirus prevalence and risk factors in a rural community in western Kenya

Alphaviruses, such as chikungunya virus, and flaviviruses, such as dengue virus, are (re)-emerging arboviruses that are endemic in tropical environments. In Africa, arbovirus infections are often undiagnosed and unreported, with febrile illnesses often assumed to be malaria. This cross-sectional study aimed to characterize the seroprevalence of alphaviruses and flaviviruses among children (ages 5-14, n = 250) and adults (ages 15 ≥ 75, n = 250) in western Kenya. Risk factors for seropositivity were explored using Lasso regression. Overall, 67% of participants showed alphavirus seropositivity (CI95 63%-70%), and 1.6% of participants showed flavivirus seropositivity (CI95 0.7%-3%). Children aged 10-14 were more likely to be seropositive to an alphavirus than adults (p < 0.001), suggesting a recent transmission period. Alphavirus and flavivirus seropositivity was detected in the youngest participants (age 5-9), providing evidence of inter-epidemic transmission. Demographic variables that were significantly different amongst those with previous infection versus those without infection included age, education level, and occupation. Behavioral and environmental variables significantly different amongst those in with previous infection to those without infection included taking animals for grazing, fishing, and recent village flooding. Experience of recent fever was also found to be a significant indicator of infection (p = 0.027). These results confirm alphavirus and flavivirus exposure in western Kenya, while illustrating significantly higher alphavirus transmission compared to previous studies.

Modeling Impact of Temperature and Human Movement on the Persistence of Dengue Disease

Dengue is a vector-borne infectious disease endemic in many parts of the world. The disease is spreading in new places due to human movement into the dengue disease supporting areas. Temperature is the major climatic factor which affects the biological processes of the mosquitoes and their interaction with the viruses. In the present work, we propose a multipatch model to assess the impact of temperature and human movement in the transmission dynamics of dengue disease. The work consists of system of ordinary differential equations that describe the transmission dynamics of dengue disease between humans and mosquitoes. Human population is divided into four classes: susceptible, exposed, infectious, and recovered. Mosquito population is divided into three classes: susceptible, exposed, and infectious. Basic reproduction number ℛ₀ of the model is obtained using Next-Generation Matrix method. The qualitative analysis of the model is made in terms of the basic reproduction number. Parameters used in the model are considered temperature dependent. Dynamics of vector and host populations are investigated with different human movement rates and different temperature levels. Numerical results show that proper management of human movement between patches helps reducing the burden of dengue disease. It is also seen that the temperature affects the transmission dynamics of the disease significantly.

Effect of Rainfall for the Dynamical Transmission Model of the Dengue Disease in Thailand

The SEIR (Susceptible-Exposed-Infected-Recovered) model is used to describe the transmission of dengue virus. The main contribution is determining the role of the rainfall in Thailand in the model. The transmission of dengue disease is assumed to depend on the nature of the rainfall in Thailand. We analyze the dynamic transmission of dengue disease. The stability of the solution of the model is analyzed. It is investigated by using the Routh-Hurwitz criteria. We find two equilibrium states: a disease-free state and an endemic equilibrium state. The basic reproductive number (R₀) is obtained, which indicates the stability of each equilibrium state. Numerical results taking into account the rainfall are obtained and they are seen to correspond to the analytical results.

Assessment of optimal strategies in a two-patch dengue transmission model with seasonality

Emerging and re-emerging dengue fever has posed serious problems to public health officials in many tropical and subtropical countries. Continuous traveling in seasonally varying areas makes it more difficult to control the spread of dengue fever. In this work, we consider a two-patch dengue model that can capture the movement of host individuals between and within patches using a residence-time matrix. A previous two-patch dengue model without seasonality is extended by adding host demographics and seasonal forcing in the transmission rates. We investigate the effects of human movement and seasonality on the two-patch dengue transmission dynamics. Motivated by the recent Peruvian dengue data in jungle/rural areas and coast/urban areas, our model mimics the seasonal patterns of dengue outbreaks in two patches. The roles of seasonality and residence-time configurations are highlighted in terms of the seasonal reproduction number and cumulative incidence. Moreover, optimal control theory is employed to identify and evaluate patch-specific control measures aimed at reducing dengue prevalence in the presence of seasonality. Our findings demonstrate that optimal patch-specific control strategies are sensitive to seasonality and residence-time scenarios. Targeting only the jungle (or endemic) is as effective as controlling both patches under weak coupling or symmetric mobility. However, focusing on intervention for the city (or high density areas) turns out to be optimal when two patches are strongly coupled with asymmetric mobility.

How does the dengue vector mosquito Aedes albopictus respond to global warming?

Background

Global warming has a marked influence on the life cycle of epidemic vectors as well as their interactions with human beings. The Aedes albopictus mosquito as the vector of dengue fever surged exponentially in the last decade, raising ecological and epistemological concerns of how climate change altered its growth rate and population dynamics. As the global warming pattern is considerably uneven across four seasons, with a confirmed stronger effect in winter, an emerging need arises as to exploring how the seasonal warming effects influence the annual development of Ae. albopictus.

Methods

The model consolidates a 35-year climate dataset and designs fifteen warming patterns that increase the temperature of selected seasons. Based on a recently developed mechanistic population model of Ae. albopictus, the model simulates the thermal reaction of blood-fed adults by systematically increasing the temperature from 0.5 to 5 °C at an interval of 0.5 °C in each warming pattern.

Results

The results show the warming effects are different across seasons. The warming effects in spring and winter facilitate the development of the species by shortening the diapause period. The warming effect in summer is primarily negative by inhibiting mosquito development. The warming effect in autumn is considerably mixed. However, these warming effects cannot carry over to the following year, possibly due to the fact that under the extreme weather in winter the mosquito fully ceases from development and survives in terms of diapause eggs.

Conclusions

As the historical pattern of global warming manifests seasonal fluctuations, this study provides corroborating and previously ignored evidence of how such seasonality affects the mosquito development. Understanding this short-term temperature-driven mechanism as one chain of the transmission events is critical to refining the thermal reaction norms of the epidemic vector under global warming as well as developing effective mosquito prevention and control strategies.

Electronic supplementary material

The online version of this article (doi:10.1186/s13071-017-2071-2) contains supplementary material, which is available to authorized users.

Age at menarche in relation to prenatal rainy season exposure and altitude of residence: results from a nationally representative survey in a tropical country

Intrauterine exposure to the rainy season in the tropics may be accompanied by high rates of infection and nutritional deficiencies. It is unknown whether this exposure is related to the extrauterine timing of development. Our aim was to evaluate the relations of prenatal exposure to the rainy season and altitude of residence with age at menarche. The study included 15,370 girls 10 to <18 years old who participated in Colombia's 2010 National Nutrition Survey. Primary exposures included the number of days exposed to the rainy season during the 40 weeks preceding birth, and altitude of residence at the time of the survey. We estimated median menarcheal ages and hazard ratios with 95% confidence interval (CI) according to exposure categories using Kaplan-Meier cumulative probabilities and Cox proportional hazards models, respectively. All tests incorporated the complex survey design. Girls in the highest quintile of gestation days exposed to the rainy season had an earlier age at menarche compared with those in the lowest (adjusted hazard ratios (HR)=1.08; 95% CI 1.00-1.18, P-trend=0.03). Girls living at altitudes ⩾2000 m had a later age at menarche compared with those living <1000 m (adjusted HR=0.88; 95% CI 0.82-0.94, P-trend <0.001). The inverse association between gestation days during the rainy season and menarche was most apparent among girls living at altitudes ⩾2000 m (P, interaction=0.04). Gestation days exposed to the rainy season and altitude of residence were associated with the timing of sexual maturation among Colombian girls independent of socioeconomic status and ethnicity.

The Driving Force for 2014 Dengue Outbreak in Guangdong, China

Dengue fever has rapidly spread in recent decades to become the most globally expansive viral vector-borne disease. In mainland China, a number of dengue outbreaks have been reported since 1978, but the worst epidemic in decades, involving 45230 cases and 76 imported cases, resulting in six deaths in Guangdong province, emerged in 2014. Reasons for this ongoing surge in dengue, both imported and autochthonous, are currently unclear and demand urgent investigation. Here, a seasonally-driven dynamic epidemiological model was used to simulate dengue transmission data recorded from the unprecedented outbreak. Sensitivity analysis demonstrate that delayed mosquito control, the continuous importations between the end of April to the early of July, the transmission of asymptomatic dengue infections, and the abnormally high precipitation from May to August might be the causal factors for the unprecedented outbreak. Our results suggested that the earlier and more frequent control measures in targeting immature and adult mosquitoes were effective in preventing larger outbreaks, and enhanced frontier health and quarantine from the end of April to the early of July for international communications and travelers.