Models of the impact of dengue vaccines: a review of current research and potential approaches

Mathematical modeling of Dengue virus serotypes propagation in Mexico

The Dengue virus (DENV) constitutes a major vector borne virus disease worldwide. Prediction of the DENV spread dynamics, prevalence and infection rates are crucial elements to guide the public health services effort towards meaningful actions. The existence of four DENV serotypes further complicates the virus proliferation forecast. The different serotypes have varying clinical impacts, and the symptomatology of the infection is dependent on the infection history of the patient. Therefore, changes in the prevalent DENV serotype found in one location have a profound impact on the regional public health. The prediction of the spread and intensity of infection of the individual DENV serotypes in specific locations would allow the authorities to plan local pesticide spray to control the vector as well as the purchase of specific antibody therapy. Here we used a mathematical model to predict serotype-specific DENV prevalence and overall case burden in Mexico.

Applying a multi-strain dengue model to epidemics data

Dengue disease transmission is a complex vector-borne disease, mainly due to the co-circulation of four serotypes of the virus. Mathematical models have proved to be a useful tool to understand the complexity of this disease. In this work, we extend the model studied by Esteva et al., 2003, originally proposed for two serotypes, to four circulating serotypes. Using epidemic data of dengue fever in Iquitos (Peru) and San Juan (Puerto Rico), we estimate numerically the co-circulation parameter values for selected outbreaks using a bootstrap method, and we also obtained the Basic Reproduction Number, R₀, for each serotype, using both analytical calculations and numerical simulations. Our results indicate that the impact of co-circulation of serotypes in population dynamics of dengue infection is such that there is a reduced effect from DENV-3 to DENV-4 in comparison to no-cross effect for epidemics in Iquitos. Concerning San Juan epidemics, also comparing to no-cross effect, we also observed a reduced effect from the predominant serotype DENV-3 to both DENV-2 and DENV-1 epidemics neglecting the very small number of cases of DENV-4.

Mathematical modeling in perspective of vector-borne viral infections: a review

Background

Viral diseases are highly widespread infections caused by viruses. These viruses are passing from one human to other humans through a certain medium. The medium might be mosquito, animal, reservoir and food, etc. Here, the population of both human and mosquito vectors are important.

Main body of the abstract

The main objectives are here to introduce the historical perspective of mathematical modeling, enable the mathematical modeler to understand the basic mathematical theory behind this and present a systematic review on mathematical modeling for four vector-borne viral diseases using the deterministic approach. Furthermore, we also introduced other mathematical techniques to deal with vector-borne diseases. Mathematical models could help forecast the infectious population of humans and vectors during the outbreak.

Short conclusion

This study will be helpful for mathematical modelers in vector-borne diseases and ready-made material in the review for future advancement in the subject. This study will not only benefit vector-borne conditions but will enable ideas for other illnesses.

Mathematical models for dengue fever epidemiology: A 10-year systematic review

Mathematical models have a long history in epidemiological research, and as the COVID-19 pandemic progressed, research on mathematical modeling became imperative and very influential to understand the epidemiological dynamics of disease spreading. Mathematical models describing dengue fever epidemiological dynamics are found back from 1970. Dengue fever is a viral mosquito-borne infection caused by four antigenically related but distinct serotypes (DENV-1 to DENV-4). With 2.5 billion people at risk of acquiring the infection, it is a major international public health concern. Although most of the cases are asymptomatic or mild, the disease immunological response is complex, with severe disease linked to the antibody-dependent enhancement (ADE) - a disease augmentation phenomenon where pre-existing antibodies to previous dengue infection do not neutralize but rather enhance the new infection. Here, we present a 10-year systematic review on mathematical models for dengue fever epidemiology. Specifically, we review multi-strain frameworks describing host-to-host and vector-host transmission models and within-host models describing viral replication and the respective immune response. Following a detailed literature search in standard scientific databases, different mathematical models in terms of their scope, analytical approach and structural form, including model validation and parameter estimation using empirical data, are described and analyzed. Aiming to identify a consensus on infectious diseases modeling aspects that can contribute to public health authorities for disease control, we revise the current understanding of epidemiological and immunological factors influencing the transmission dynamics of dengue. This review provide insights on general features to be considered to model aspects of real-world public health problems, such as the current epidemiological scenario we are living in.

Case-area targeted interventions (CATI) for reactive dengue control: Modelling effectiveness of vector control and prophylactic drugs in Singapore

BACKGROUND

Targeting interventions to areas that have recently experienced cases of disease is one strategy to contain outbreaks of infectious disease. Such case-area targeted interventions (CATI) have become an increasingly popular approach for dengue control but there is little evidence to suggest how precisely targeted or how recent cases need to be, to mount an effective response. The growing interest in the development of prophylactic and therapeutic drugs for dengue has also given new relevance for CATI strategies to interrupt transmission or deliver early treatment.

METHODS/PRINCIPAL FINDINGS

Here we develop a patch-based mathematical model of spatial dengue spread and fit it to spatiotemporal datasets from Singapore. Simulations from this model suggest CATI strategies could be effective, particularly if used in lower density areas. To maximise effectiveness, increasing the size of the radius around an index case should be prioritised even if it results in delays in the intervention being applied. This is partially because large intervention radii ensure individuals receive multiple and regular rounds of drug dosing or vector control, and thus boost overall coverage. Given equivalent efficacy, CATIs using prophylactic drugs are predicted to be more effective than adult mosquito-killing vector control methods and may even offer the possibility of interrupting individual chains of transmission if rapidly deployed. CATI strategies quickly lose their effectiveness if baseline transmission increases or case detection rates fall.

CONCLUSIONS/SIGNIFICANCE

These results suggest CATI strategies can play an important role in dengue control but are likely to be most relevant for low transmission areas where high coverage of other non-reactive interventions already exists. Controlled field trials are needed to assess the field efficacy and practical constraints of large operational CATI strategies.

Vector Surveillance, Host Species Richness, and Demographic Factors as West Nile Disease Risk Indicators

West Nile virus (WNV) is the most common arthropod-borne virus (arbovirus) in the United States (US) and is the leading cause of viral encephalitis in the country. The virus has affected tens of thousands of US persons total since its 1999 North America introduction, with thousands of new infections reported annually. Approximately 1% of humans infected with WNV acquire neuroinvasive West Nile Disease (WND) with severe encephalitis and risk of death. Research describing WNV ecology is needed to improve public health surveillance, monitoring, and risk assessment. We applied Bayesian joint-spatiotemporal modeling to assess the association of vector surveillance data, host species richness, and a variety of other environmental and socioeconomic disease risk factors with neuroinvasive WND throughout the conterminous US. Our research revealed that an aging human population was the strongest disease indicator, but climatic and vector-host biotic interactions were also significant in determining risk of neuroinvasive WND. Our analysis also identified a geographic region of disproportionately high neuroinvasive WND disease risk that parallels the Continental Divide, and extends southward from the US-Canada border in the states of Montana, North Dakota, and Wisconsin to the US-Mexico border in western Texas. Our results aid in unraveling complex WNV ecology and can be applied to prioritize disease surveillance locations and risk assessment.

Distinct Dengue Disease Epidemiology, Clinical, and Diagnosis Features in Western, Central, and Eastern Regions of Indonesia, 2017-2019

The people of Indonesia have been afflicted by dengue, a mosquito-borne viral disease, for over 5 decades. The country is the world's largest archipelago with diverse geographic, climatic, and demographic conditions that may impact the dynamics of disease transmissions. A dengue epidemiology study was launched by us to compare and understand the dynamics of dengue and other arboviral diseases in three cities representing western, central, and eastern Indonesia, namely, Batam, Banjarmasin, and Ambon, respectively. A total of 732 febrile patients were recruited with dengue-like illness during September 2017-2019 and an analysis of their demographic, clinical, and virological features was performed. The seasonal patterns of dengue-like illness were found to be different in the three regions. Among all patients, 271 (37.0%) were virologically confirmed dengue, while 152 (20.8%) patients were diagnosed with probable dengue, giving a total number of 423 (57.8%) dengue patients. Patients' age and clinical manifestations also differed between cities. Mostly, mild dengue fever was observed in Batam, while more severe cases were prominent in Ambon. While all dengue virus (DENV) serotypes were detected, distinct serotypes dominated in different locations: DENV-1 in Batam and Ambon, and DENV-3 in Banjarmasin. We also assessed the diagnostic features in the study sites, which revealed different patterns of diagnostic agreements, particularly in Ambon. To detect the possibility of infection with other arboviruses, further testing on 461 DENV RT-PCR-negative samples was performed using pan-flavivirus and -alphavirus RT-PCRs; however, only one chikungunya infection was detected in Ambon. A diverse dengue epidemiology in western, central, and eastern Indonesia was observed, which is likely to be influenced by local geographic, climatic, and demographic conditions, as well as differences in the quality of healthcare providers and facilities. Our study adds a new understanding on dengue epidemiology in Indonesia.

Implementation of geographical information systems for the study of diseases caused by vector-borne arboviruses in Southeast Asia: A review based on the publication record

The spread of mosquito-borne diseases in Southeast Asia has dramatically increased in the latest decades. These infections include dengue, chikungunya and Japanese Encephalitis (JE), high-burden viruses sharing overlapping disease manifestation and vector distribution. The use of Geographical Information Systems (GIS) to monitor the dynamics of disease and vector distribution can assist in disease epidemic prediction and public health interventions, particularly in Southeast Asia where sustained high temperatures drive the epidemic spread of these mosquito-borne viruses. Due to lack of accurate data, the spatial and temporal dynamics of these mosquito-borne viral disease transmission countries are poorly understood, which has limited disease control effort. By following studies carried out on these three viruses across the region in a specific time period revealing general patterns of research activities and characteristics, this review finds the need to improve decision-support by disease mapping and management. The results presented, based on a publication search with respect to diseases due to arboviruses, specifically dengue, chikungunya and Japanese encephalitis, should improve opportunities for future studies on the implementation of GIS in the control of mosquito-borne viral diseases in Southeast Asia.

Estimate of the Basic Reproduction Number for COVID-19: A Systematic Review and Meta-analysis

OBJECTIVES

The outbreak of coronavirus disease 2019 (COVID-19) is one of the main public health challenges currently facing the world. Because of its high transmissibility, COVID-19 has already caused extensive morbidity and mortality in many countries throughout the world. An accurate estimation of the basic reproduction number (R0) of COVID-19 would be beneficial for prevention programs. In light of discrepancies in original research on this issue, this systematic review and meta-analysis aimed to estimate the pooled R0 for COVID-19 in the current outbreak.

METHODS

International databases (including Google Scholar, Science Direct, PubMed, and Scopus) were searched to identify studies conducted regarding the R0 of COVID-19. Articles were searched using the following keywords: "COVID-19" and "basic reproduction number" or "R0." The heterogeneity among studies was assessed using the I2 index, the Cochran Q test, and T2. A random-effects model was used to estimate R0 in this study.

RESULTS

The mean reported R0 in the identified articles was 3.38±1.40, with a range of 1.90 to 6.49. According to the results of the random-effects model, the pooled R0 for COVID-19 was estimated as 3.32 (95% confidence interval, 2.81 to 3.82). According to the results of the meta-regression analysis, the type of model used to estimate R0 did not have a significant effect on heterogeneity among studies (p=0.81).

CONCLUSIONS

Considering the estimated R0 for COVID-19, reducing the number of contacts within the population is a necessary step to control the epidemic. The estimated overall R0 was higher than the World Health Organization estimate.

Sensitivity, uncertainty and identifiability analyses to define a dengue transmission model with real data of an endemic municipality of Colombia

Dengue disease is a major problem for public health surveillance entities in tropical and subtropical regions having a significant impact not only epidemiological but social and economical. There are many factors involved in the dengue transmission process. We can evaluate the importance of these factors through the formulation of mathematical models. However, the majority of the models presented in the literature tend to be overparameterized, with considerable uncertainty levels and excessively complex formulations. We aim to evaluate the structure, complexity, trustworthiness, and suitability of three models, for the transmission of dengue disease, through different strategies. To achieve this goal, we perform structural and practical identifiability, sensitivity and uncertainty analyses to these models. The results showed that the simplest model was the most appropriate and reliable when the only available information to fit them is the cumulative number of reported dengue cases in an endemic municipality of Colombia.

Designing effective control of dengue with combined interventions

Viruses transmitted by Aedes mosquitoes, such as dengue, Zika, and chikungunya, have expanding ranges and seem unabated by current vector control programs. Effective control of these pathogens likely requires integrated approaches. We evaluated dengue management options in an endemic setting that combine novel vector control and vaccination using an agent-based model for Yucatán, Mexico, fit to 37 y of data. Our intervention models are informed by targeted indoor residual spraying (TIRS) experiments; trial outcomes and World Health Organization (WHO) testing guidance for the only licensed dengue vaccine, CYD-TDV; and preliminary results for in-development vaccines. We evaluated several implementation options, including varying coverage levels; staggered introductions; and a one-time, large-scale vaccination campaign. We found that CYD-TDV and TIRS interfere: while the combination outperforms either alone, performance is lower than estimated from their separate benefits. The conventional model hypothesized for in-development vaccines, however, performs synergistically with TIRS, amplifying effectiveness well beyond their independent impacts. If the preliminary performance by either of the in-development vaccines is upheld, a one-time, large-scale campaign followed by routine vaccination alongside aggressive new vector control could enable short-term elimination, with nearly all cases avoided for a decade despite continuous dengue reintroductions. If elimination is impracticable due to resource limitations, less ambitious implementations of this combination still produce amplified, longer-lasting effectiveness over single-approach interventions.

Spatial Dynamics of Chikungunya Virus, Venezuela, 2014

Since chikungunya virus emerged in the Caribbean region in late 2013, ≈45 countries have experienced chikungunya outbreaks. We described and quantified the spatial and temporal events after the introduction and propagation of chikungunya into an immunologically naive population from the urban north-central region of Venezuela during 2014. The epidemic curve (n = 810 cases) unraveled within 5 months with a basic reproductive number of 3.7 and a radial spread traveled distance of 9.4 km at a mean velocity of 82.9 m/day. The highest disease diffusion speed occurred during the first 90 days, and space and space-time modeling suggest the epidemic followed a particular geographic pathway with spatiotemporal aggregation. The directionality and heterogeneity of transmission during the first introduction of chikungunya indicated existence of areas of diffusion and elevated risk for disease and highlight the importance of epidemic preparedness. This information will help in managing future threats of new or reemerging arboviruses.

An open challenge to advance probabilistic forecasting for dengue epidemics

A wide range of research has promised new tools for forecasting infectious disease dynamics, but little of that research is currently being applied in practice, because tools do not address key public health needs, do not produce probabilistic forecasts, have not been evaluated on external data, or do not provide sufficient forecast skill to be useful. We developed an open collaborative forecasting challenge to assess probabilistic forecasts for seasonal epidemics of dengue, a major global public health problem. Sixteen teams used a variety of methods and data to generate forecasts for 3 epidemiological targets (peak incidence, the week of the peak, and total incidence) over 8 dengue seasons in Iquitos, Peru and San Juan, Puerto Rico. Forecast skill was highly variable across teams and targets. While numerous forecasts showed high skill for midseason situational awareness, early season skill was low, and skill was generally lowest for high incidence seasons, those for which forecasts would be most valuable. A comparison of modeling approaches revealed that average forecast skill was lower for models including biologically meaningful data and mechanisms and that both multimodel and multiteam ensemble forecasts consistently outperformed individual model forecasts. Leveraging these insights, data, and the forecasting framework will be critical to improve forecast skill and the application of forecasts in real time for epidemic preparedness and response. Moreover, key components of this project-integration with public health needs, a common forecasting framework, shared and standardized data, and open participation-can help advance infectious disease forecasting beyond dengue.

Incidence dynamics and investigation of key interventions in a dengue outbreak in Ningbo City, China

Background

The reported incidence of dengue fever increased dramatically in recent years in China. This study aimed to investigate and to assess the effectiveness of intervention implemented in a dengue outbreak in Ningbo City, Zhejiang Province, China.

Methods

Data of a dengue outbreak were collected in Ningbo City in China by a field epidemiological survey according to a strict protocol and case definition. Serum specimens of all cases were collected for diagnosis and the virological characteristics were detected by using polymerase chain reaction (PCR) and gene sequencing. Vector surveillance was implemented during the outbreak to collect the larva and adult mosquito densities to calculate the Breteau Index (BI) and human biting rate (HBR), respectively. Data of monthly BI and light-trap density in 2018 were built to calculate the seasonality of the vector. A transmission mathematical model was developed to dynamic the incidence of the disease. The parameters of the model were estimated by the data of the outbreak and vector surveillance data in 2018. The effectiveness of the interventions implemented during the outbreak was assessed by the data and the modelling.

Results

From 11 August to 8 September, 2018, a dengue outbreak was reported with 27 confirmed cases in a population of 5536-people community (community A) of Ningbo City. Whole E gene sequences were obtained from 24 cases and were confirmed as dengue virus type 1 (DENV-1). The transmission source of the outbreak was origin from community B where a dengue case having the same E gene sequence was onset on 30 July. Aedes albopictus was the only vector species in the area. The value of BI and HBR was 57.5 and 12 per person per hour respectively on 18 August, 2018 and decreased dramatically after interventions. The transmission model fitted well (χ² = 6.324, P = 0.388) with the reported cases data. With no intervention, the total simulated number of the cases would be 1728 with a total attack rate (TAR) of 31.21% (95%CI: 29.99%– 32.43%). Case isolation and larva control (LC) have almost the same TAR and duration of outbreak (DO) as no intervention. Different levels of reducing HBR (rHBR) had different effectiveness with TARs ranging from 1.05% to 31.21% and DOs ranging from 27 days to 102 days. Adult vector control (AVC) had a very low TAR and DO. “LC+AVC” had a similar TAR and DO as that of AVC. “rHBR_100%+LC”, “rHBR_100%+AVC”, “rHBR_100%+LC+AVC” and “rHBR_100%+LC+AVC+Iso” had the same effectiveness.

Conclusions

Without intervention, DENV-1 could be transmitted rapidly within a short period of time and leads to high attack rate in community in China. AVC or rHBR should be recommended as primary interventions to control rapid transmission of the dengue virus at the early stage of an outbreak.

Dengue has led to heavy disease burden in China. The reported incidence of the disease increased dramatically in recent years and cases have expanded from southern to central and northern part of China. In this study, the findings include that DENV-1 can transmit rapidly with a short period of time and leads to high attack rate in community, and that rHBR or AVC should be recommended as primary interventions to control rapid transmission of dengue virus at the early stage of an outbreak. Therefore, dengue outbreak is at high risk in many areas in China because of the potential high receptivity (widely distribution of Ae. albopictus) and vulnerability (high frequency of the importation) of the transmission. The high transmissibility of the virus makes it hard and urgent to control the outbreak. Delayed intervention (larvae control or case isolation) is hard to show its effectiveness and the interventions without delay are strongly recommended. Bed net or mosquito repellents were encouraged to use in the community to reduce HBR, and space spraying of insecticides were recommended to control adult vector during the outbreak.

Multiple introductions of dengue virus strains contribute to dengue outbreaks in East Kalimantan, Indonesia, in 2015-2016

BACKGROUND

Dengue fever is a febrile disease caused by dengue virus (DENV), which affects people throughout the tropical and subtropical regions of the world, including Indonesia. East Kalimantan (Borneo) province suffered a dramatic increase in dengue cases in 2015 and 2016, making it the province with the second highest incidence of dengue in Indonesia. Despite this, dengue in East Kalimantan is understudied; leaving transmission dynamics of the disease in the area are mostly unknown. In this study, we investigate the factors contributing to the outbreaks in East Kalimantan.

METHODS

Prospective clinical and molecular virology study was conducted in two main cities in the province, namely Samarinda and Balikpapan, in 2015-2016. Patients' clinical, hematological, and demographic data were recorded. Dengue detection and confirmation was performed using NS1-antigen and IgG/IgM antibody detection. RT-PCR was conducted to determine the serotypes of the virus. Phylogenetic analysis was performed based on envelope gene sequences.

RESULTS

Three hundred patients with suspected dengue were recruited. Among these, 132 (44%) were diagnosed with dengue by NS1 antigen and/or nucleic acid detection. The majority of the infections (60%) were primary, with dengue hemorrhagic fever (DHF) the predominant manifestation (71.9%). Serotyping detected all four DENV serotypes in 112 (37.3%) cases, with the majority of patients (58.9%) infected by DENV-3. Phylogenetic analysis based on envelope gene sequences revealed the genotypes of the viruses as DENV-1 Genotype I, DENV-2 Cosmopolitan, and DENV-3 Genotype I. Most virus strains were closely-related to strains from cities in Indonesia.

CONCLUSIONS

Our observations indicate that multiple introductions of endemic DENV from surrounding cities in Indonesia, coupled with relatively low herd immunity, were likely responsible for the outbreak of the dominant viruses. The study provides information on the clinical spectrum of the disease, together with serology, viral genetics, and demographic data, which will be useful for better understanding of dengue disease in Borneo.

Mathematical modeling of dengue epidemic: control methods and vaccination strategies

Dengue is, in terms of death and economic cost, one of the most important infectious diseases in the world. So, its mathematical modeling can be a valuable tool to help us to understand the dynamics of the disease and to infer about its spreading by the proposition of control methods. In this paper, control strategies, which aim to eliminate the Aedes aegypti mosquito, as well as proposals for the vaccination campaign are evaluated. In our mathematical model, the mechanical control is accomplished through the environmental support capacity affected by a discrete function that represents the removal of breedings. Chemical control is carried out using insecticide and larvicide. The efficiency of vaccination is studied through the transfer of a fraction of individuals, proportional to the vaccination rate, from the susceptible to the recovered compartments. Our major find is that the dengue fever epidemic is only eradicated with the use of an immunizing vaccine because control measures, directed against its vector, are not enough to halt the disease spreading. Even when the infected mosquitoes are eliminated from the system, the susceptible ones are still present, and infected humans cause dengue fever to reappear in the human population.

Comparison of stochastic and deterministic frameworks in dengue modelling

We perform estimations of compartment models for dengue transmission in rural Cambodia with increasing complexity regarding both model structure and the account for stochasticity. On the one hand, we successively account for three embedded sources of stochasticity: observation noise, demographic variability and environmental hazard. On the other hand, complexity in the model structure is increased by introducing vector-borne transmission, explicit asymptomatic infections and interacting virus serotypes. Using two sources of case data from dengue epidemics in Kampong Cham (Cambodia), models are estimated in the bayesian framework, with Markov Chain Monte Carlo and Particle Markov Chain Monte Carlo. We highlight the advantages and drawbacks of the different formulations in a practical setting. Although in this case the deterministic models provide a good approximation of the mean trajectory for a low computational cost, the stochastic frameworks better reflect and account for parameter and simulation uncertainty.

Analysis of Zika virus neutralizing antibodies in normal healthy Thais

Zika virus (ZIKV) infections have been reported from all over Thailand, but the number of reported cases remains low, suggesting a degree of immune protection against ZIKV infection. To address this possibility, the presence of ZIKV neutralizing antibodies was determined in serum from 135 healthy Thai adults with a plaque reduction neutralization test (PRNT), and a number of samples were subsequently analyzed for the presence of neutralizing antibodies to dengue virus (DENV) and Japanese encephalitis virus (JEV). Results showed that 70.4% (PRNT50 ≥ 10), 55.6 (PRNT50 ≥ 20) or 22.2% (PRNT90 ≥ 20) of the samples showed neutralizing antibodies to ZIKV. Detailed analysis showed no association between the presence of neutralizing antibodies to other flaviviruses (DENV, JEV) and the presence of ZIKV neutralizing antibodies. These results suggest that the level of ZIKV neutralizing antibodies in the Thai population is enough to dampen the transmission of the virus in Thailand.

Basic reproduction number for the Brazilian Spotted Fever

Brazilian Spotted Fever (BSF) is an emerging and lethal disease in South America which basic reproduction number (R₀) is unknown. Calculating R₀ for this disease is crucial to design control interventions and prevent human deaths. BSF endemic areas are related to the presence of capybaras Hydrochoerus hydrochaeris, amplifier hosts of Rickettsia rickettsii and primary hosts of the tick Amblyomma sculptum, main vector of the agent in this area. Because of the complexity of its dynamics, we calculated R₀ for the BSF system by constructing a next-generation matrix considering different categories of vectors and hosts. Each matrix element was considered as the expected number of infected individuals of one category produced by a single infected individual of a second category. We used field and experimental data to parameterize the next-generation matrix and obtain the final calculation (R₀ ≈ 1.7). We demonstrated the low impact of the matrix elements corresponding to the transovarial transmission and the transmission from infected larvae in the maintenance of R. rickettsii. Sensitivity and elasticity analyzes were performed to quantify the perturbations of each matrix element in R₀. We noted that the elements equivalent to the number of infected attached nymphs produced by an infected capybara, and the number of infected capybaras produced by an infected attached nymph or adult are the major contributors to changes in R₀. Our results provide insights into the strategic design of preventive interventions in BSF endemic areas.

Modeling the Heterogeneity of Dengue Transmission in a City

Dengue fever is one of the most important vector-borne diseases in the world, and modeling its transmission dynamics allows for determining the key influence factors and helps to perform interventions. The heterogeneity of mosquito bites of humans during the spread of dengue virus is an important factor that should be considered when modeling the dynamics. However, traditional models generally assumed homogeneous mixing between humans and vectors, which is inconsistent with reality. In this study, we proposed a compartmental model with negative binomial distribution transmission terms to model this heterogeneity at the population level. By including the aquatic stage of mosquitoes and incorporating the impacts of the environment and climate factors, an extended model was used to simulate the 2014 dengue outbreak in Guangzhou, China, and to simulate the spread of dengue in different scenarios. The results showed that a high level of heterogeneity can result in a small peak size in an outbreak. As the level of heterogeneity decreases, the transmission dynamics approximate the dynamics predicted by the corresponding homogeneous mixing model. The simulation results from different scenarios showed that performing interventions early and decreasing the carrying capacity for mosquitoes are necessary for preventing and controlling dengue epidemics. This study contributes to a better understanding of the impact of heterogeneity during the spread of dengue virus.

Seroprevalence of Dengue Antibodies in Three Urban Settings in Yucatan, Mexico

Dengue transmission in Mexico has become a major public health problem. Few epidemiological studies have examined the seroprevalence of dengue in Mexico, and recent estimates are needed to better understand dengue transmission dynamics. We conducted a dengue seroprevalence survey among 1,668 individuals including all age groups in three urban settings in Yucatan, Mexico. Children (< 19 years old) were selected randomly from schools. The adults (≥ 19 years old) were selected from healthcare facilities. Participants were asked to provide a venous blood sample and to answer a brief questionnaire with demographic information. Previous exposure to dengue was determined using indirect immunoglobulin G enzyme-linked immunosorbent assay. The overall seroprevalence was 73.6%. The age-specific seroprevalence increased with age, going from 51.4% (95% confidence interval [CI] = 45.0-57.9%) in children ≤ 8 years to 72% (95% CI = 66.3-77.2%) in the 9- to 14-years old. The highest seroprevalence was 83.4% (95% CI = 77-82.2%) in adults greater than 50 years. The seroprevalence in Merida was 68.6% (95% CI = 65-72%), in Progreso 68.7% (95% CI = 64.2-72.8%), and in Ticul 85.3% (95% CI = 81.9-88.3%). Ticul had the highest seroprevalence in all age groups. Logistic regression analysis showed that age and city of residence were associated with greater risk of prior dengue exposure. The results highlight the level of past exposure to dengue virus including young children. Similar studies should be conducted elsewhere in Mexico and other endemic countries to better understand the transmission dynamics of dengue.

Using Wolbachia for Dengue Control: Insights from Modelling

Dengue is the most common arboviral infection of humans, responsible for a substantial disease burden across the tropics. Traditional insecticide-based vector-control programmes have limited effectiveness, and the one licensed vaccine has a complex and imperfect efficacy profile. Strains of the bacterium Wolbachia, deliberately introduced into Aedes aegyptimosquitoes, have been shown to be able to spread to high frequencies in mosquito populations in release trials, and mosquitoes infected with these strains show markedly reduced vector competence. Thus, Wolbachia represents an exciting potential new form of biocontrol for arboviral diseases, including dengue. Here, we review how mathematical models give insight into the dynamics of the spread of Wolbachia, the potential impact of Wolbachia on dengue transmission, and we discuss the remaining challenges in evaluation and development.

An exploration of the role of asymptomatic infections in the epidemiology of dengue viruses through susceptible, asymptomatic, infected and recovered (SAIR) models

It is estimated that 20-97% of all dengue infections could be asymptomatic. I used SIR models to investigate the epidemiological role of such infections, by adding an asymptomatic class (SAIR models). Upon infection in one of the models, a human becomes either symptomatic or asymptomatic. In the other, a human becomes asymptomatic and may progress to being symptomatic. The robustness of results from these models is examined by incorporating the mosquito-vector into one of the models, followed by simulating epidemic dynamics stochastically. Results from the first two models were very similar, with epidemics typically lasting less than one year. When mosquitoes were explicitly modelled in a high-transmission setting, if the level or duration of infectivity from asymptomatic infections was high relative to symptomatic infections, dengue would become endemic. Under stochastic simulation this effect of asymptomatic infections leading to dengue persisting was no longer guaranteed. Longer durations in asymptomatic infections had a higher chance of causing dengue's persistence in stochastic simulation, indicating that this may be more of a key determinant for dengue's persistence to 10 years than the infectivity of such infections. Otherwise, the level and duration of infectivity from asymptomatic infections had similar effects on R₀ and other epidemiological measures. With all models, outbreaks often led to a larger proportion of the population being immune than suggested by monitoring symptomatic dengue infections. This population would be at risk of developing severe dengue in a subsequent outbreak with a different dengue serotype, and would have to be determined via expansion factors.

Cost-Effectiveness of Dengue Vaccination Programs in Brazil

The first approved dengue vaccine, CYD-TDV, a chimeric, live-attenuated, tetravalent dengue virus vaccine, was recently licensed in 13 countries, including Brazil. In light of recent vaccine approval, we modeled the cost-effectiveness of potential vaccination policies mathematically based on data from recent vaccine efficacy trials that indicated that vaccine efficacy was lower in seronegative individuals than in seropositive individuals. In our analysis, we investigated several vaccination programs, including routine vaccination, with various vaccine coverage levels and those with and without large catch-up campaigns. As it is unclear whether the vaccine protects against infection or just against disease, our model incorporated both direct and indirect effects of vaccination. We found that in the presence of vaccine-induced indirect protection, the cost-effectiveness of dengue vaccination decreased with increasing vaccine coverage levels because the marginal returns of herd immunity decreases with vaccine coverage. All routine dengue vaccination programs that we considered were cost-effective, reducing dengue incidence significantly. Specifically, a routine dengue vaccination of 9-year-olds would be cost-effective when the cost of vaccination per individual is less than $262. Furthermore, the combination of routine vaccination and large catch-up campaigns resulted in a greater reduction of dengue burden (by up to 93%) than routine vaccination alone, making it a cost-effective intervention as long as the cost per course of vaccination is $255 or less. Our results show that dengue vaccination would be cost-effective in Brazil even with a relatively low vaccine efficacy in seronegative individuals.

Zika Virus Seroprevalence, French Polynesia, 2014-2015

During 2013-2014, French Polynesia experienced an outbreak of Zika virus infection. Serosurveys conducted at the end of the outbreak and 18 months later showed lower than expected disease prevalence rates (49%) and asymptomatic:symptomatic case ratios (1:1) in the general population but significantly different prevalence rates (66%) and asymptomatic:symptomatic ratios (1:2) in schoolchildren.

Cost-effectiveness of dengue vaccination in Yucatán, Mexico using a dynamic dengue transmission model

Background

The incidence of dengue fever (DF) is steadily increasing in Mexico, burdening health systems with consequent morbidities and mortalities. On December 9^th, 2015, Mexico became the first country for which the dengue vaccine was approved for use. In anticipation of a vaccine rollout, analysis of the cost-effectiveness of the dengue vaccination program that quantifies the dynamics of disease transmission is essential.

Methods

We developed a dynamic transmission model of dengue in Yucatán, Mexico and its proposed vaccination program to incorporate herd immunity into our analysis of cost-effectiveness analysis. Our model also incorporates important characteristics of dengue epidemiology, such as clinical cross-immunity and susceptibility enhancement upon secondary infection. Using our model, we evaluated the cost-effectiveness and economic impact of an imperfect dengue vaccine in Yucatán, Mexico.

Conclusions

Our study indicates that a dengue vaccination program would prevent 90% of cases of symptomatic DF incidence as well as 90% of dengue hemorrhagic fever (DHF) incidence and dengue-related deaths annually. We conclude that a dengue vaccine program in Yucatán, Mexico would be very cost-effective as long as the vaccination cost per individual is less than $140 and $214 from health care and societal perspectives, respectively. Furthermore, at an exemplary vaccination cost of $250 USD per individual on average, dengue vaccination is likely to be cost-effective 43% and 88% of the time from health care and societal perspectives, respectively.

Comparative Analysis of Dengue and Zika Outbreaks Reveals Differences by Setting and Virus

The pacific islands of Micronesia have experienced several outbreaks of mosquito-borne diseases over the past decade. In outbreaks on small islands, the susceptible population is usually well defined, and there is no co-circulation of pathogens. Because of this, analysing such outbreaks can be useful for understanding the transmission dynamics of the pathogens involved, and particularly so for yet understudied pathogens such as Zika virus. Here, we compared three outbreaks of dengue and Zika virus in two different island settings in Micronesia, the Yap Main Islands and Fais, using a mathematical model of transmission dynamics and making full use of commonalities in disease and setting between the outbreaks. We found that the estimated reproduction numbers for Zika and dengue were similar when considered in the same setting, but that, conversely, reproduction number for the same disease can vary considerably by setting. On the Yap Main Islands, we estimated a reproduction number of 8.0-16 (95% Credible Interval (CI)) for the dengue outbreak and 4.8-14 (95% CI) for the Zika outbreak, whereas for the dengue outbreak on Fais our estimate was 28-102 (95% CI). We further found that the proportion of cases of Zika reported was smaller (95% CI 1.4%-1.9%) than that of dengue (95% CI: 47%-61%). We confirmed these results in extensive sensitivity analysis. They suggest that models for dengue transmission can be useful for estimating the predicted dynamics of Zika transmission, but care must be taken when extrapolating findings from one setting to another.

Evaluating the performance of infectious disease forecasts: A comparison of climate-driven and seasonal dengue forecasts for Mexico

Dengue viruses, which infect millions of people per year worldwide, cause large epidemics that strain healthcare systems. Despite diverse efforts to develop forecasting tools including autoregressive time series, climate-driven statistical, and mechanistic biological models, little work has been done to understand the contribution of different components to improved prediction. We developed a framework to assess and compare dengue forecasts produced from different types of models and evaluated the performance of seasonal autoregressive models with and without climate variables for forecasting dengue incidence in Mexico. Climate data did not significantly improve the predictive power of seasonal autoregressive models. Short-term and seasonal autocorrelation were key to improving short-term and long-term forecasts, respectively. Seasonal autoregressive models captured a substantial amount of dengue variability, but better models are needed to improve dengue forecasting. This framework contributes to the sparse literature of infectious disease prediction model evaluation, using state-of-the-art validation techniques such as out-of-sample testing and comparison to an appropriate reference model.

Modelling weekly vector control against Dengue in the Guangdong Province of China

We develop a mathematical model to closely mimic the integrated program of impulsive vector control (every Friday afternoon since the initiation of the program) and continuous patient treatment and isolation implemented in the Guangdong Province of China during its 2014 dengue outbreak. We fitted the data of accumulated infections and used the parameterized model to carry out a retrospective analysis to estimate the basic reproduction number 1.7425 (95% CI 1.4443-2.0408), the control reproduction number 0.1709, and the mosquito-killing ratios 0.1978, 0.2987, 0.6158 and 0.5571 on October 3, 10, 17 and 24, respectively. This suggests that integrated intervention is highly effective in controlling the dengue outbreak. We also simulated outbreak outcomes under different variations of the implemented interventions. We showed that skipping one Friday for vector control would not result in raising the control reproduction number to the threshold value 1 but would lead to significant increase in the accumulated infections at the end of the outbreak. The findings indicate that quick and persistent impulsive implementation of vector control result in an effective reduction in the control reproduction number and hence lead to significant decline of new infections.

DengueME: A Tool for the Modeling and Simulation of Dengue Spatiotemporal Dynamics

The prevention and control of dengue are great public health challenges for many countries, particularly since 2015, as other arboviruses have been observed to interact significantly with dengue virus. Different approaches and methodologies have been proposed and discussed by the research community. An important tool widely used is modeling and simulation, which help us to understand epidemic dynamics and create scenarios to support planning and decision making processes. With this aim, we proposed and developed DengueME, a collaborative open source platform to simulate dengue disease and its vector's dynamics. It supports compartmental and individual-based models, implemented over a GIS database, that represent Aedes aegypti population dynamics, human demography, human mobility, urban landscape and dengue transmission mediated by human and mosquito encounters. A user-friendly graphical interface was developed to facilitate model configuration and data input, and a library of models was developed to support teaching-learning activities. DengueME was applied in study cases and evaluated by specialists. Other improvements will be made in future work, to enhance its extensibility and usability.

Dengue Dynamics and Vaccine Cost-Effectiveness Analysis in the Philippines

Dengue is one of the most problematic vector-borne diseases in the Philippines, with an estimated 842,867 cases resulting in medical costs of $345 million U.S. dollars annually. In December 2015, the first dengue vaccine, known as chimeric yellow fever virus-dengue virus tetravalent dengue vaccine, was approved for use in the Philippines and is given to children 9 years of age. To estimate the cost-effectiveness of dengue vaccination in the Philippines, we developed an age-structured model of dengue transmission and vaccination. Using our model, we compared two vaccination scenarios entailing routine vaccination programs both with and without catch-up vaccination. Our results indicate that the higher the cost of vaccination, the less cost-effective the dengue vaccination program. With the current dengue vaccination program that vaccinates children 9 years of age, dengue vaccination is cost-effective for vaccination costs up to $70 from a health-care perspective and up to $75 from a societal perspective. Under a favorable scenario consisting of 1 year of catch-up vaccinations that target children 9-15 years of age, followed by regular vaccination of 9-year-old children, vaccination is cost-effective at costs up to $72 from a health-care perspective and up to $78 from a societal perspective. In general, dengue vaccination is expected to reduce the incidence of both dengue fever and dengue hemorrhagic fever /dengue shock syndrome. Our results demonstrate that even at relatively low vaccine efficacies, age-targeted vaccination may still be cost-effective provided the vaccination cost is sufficiently low.

Assessing dengue vaccination impact: Model challenges and future directions

In response to the sharp rise in the global burden caused by dengue virus (DENV) over the last few decades, the WHO has set out three specific key objectives in its disease control strategy: (i) to estimate the true burden of dengue by 2015; (ii) a reduction in dengue mortality by at least 50% by 2020 (used as a baseline); and (iii) a reduction in dengue morbidity by at least 25% by 2020. Although various elements will all play crucial parts in achieving this goal, from diagnosis and case management to integrated surveillance and outbreak response, sustainable vector control, vaccine implementation and finally operational and implementation research, it seems clear that new tools (e.g. a safe and effective vaccine and/or effective vector control) are key to success. The first dengue vaccine was licensed in December 2015, Dengvaxia® (CYD-TDV) developed by Sanofi Pasteur. The WHO has provided guidance on the use of CYD-TDV in endemic countries, for which there are a variety of considerations beyond the risk-benefit evaluation done by regulatory authorities, including public health impact and cost-effectiveness. Population-level vaccine impact and economic and financial aspects are two issues that can potentially be considered by means of mathematical modelling, especially for new products for which empirical data are still lacking. In December 2014 a meeting was convened by the WHO in order to revisit the current status of dengue transmission models and their utility for public health decision-making. Here, we report on the main points of discussion and the conclusions of this meeting, as well as next steps for maximising the use of mathematical models for vaccine decision-making.

Dengue serotype immune-interactions and their consequences for vaccine impact predictions

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The firstever dengue vaccine, Dengvaxia^®, has recently been licensed for use in several countries.

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Mathematical models are valuable tools for assessing vaccination impact on dengue burden.

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Model assumptions regarding dengue serotype immune interactions are inconsistent.

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Our results demonstrate how model assumptions critically affect vaccine impact predictions.

Dengue is one of the most important and wide-spread viral infections affecting human populations. The last few decades have seen a dramatic increase in the global burden of dengue, with the virus now being endemic or near-endemic in over 100 countries world-wide. A recombinant tetravalent vaccine candidate (CYD-TDV) has recently completed Phase III clinical efficacy trials in South East Asia and Latin America and has been licensed for use in several countries. The trial results showed moderate-to-high efficacies in protection against clinical symptoms and hospitalisation but with so far unknown effects on transmission and infections per se. Model-based predictions about the vaccine's short- or long-term impact on the burden of dengue are therefore subject to a considerable degree of uncertainty. Furthermore, different immune interactions between dengue's serotypes have frequently been evoked by modelling studies to underlie dengue's oscillatory dynamics in disease incidence and serotype prevalence. Here we show how model assumptions regarding immune interactions in the form of antibody-dependent enhancement, temporary cross-immunity and the number of infections required to develop full immunity can significantly affect the predicted outcome of a dengue vaccination campaign. Our results thus re-emphasise the important gap in our current knowledge concerning the effects of previous exposure on subsequent dengue infections and further suggest that intervention impact studies should be critically evaluated by their underlying assumptions about serotype immune-interactions.

Utilizing Nontraditional Data Sources for Near Real-Time Estimation of Transmission Dynamics During the 2015-2016 Colombian Zika Virus Disease Outbreak

Background

Approximately 40 countries in Central and South America have experienced local vector-born transmission of Zika virus, resulting in nearly 300,000 total reported cases of Zika virus disease to date. Of the cases that have sought care thus far in the region, more than 70,000 have been reported out of Colombia.

Objective

In this paper, we use nontraditional digital disease surveillance data via HealthMap and Google Trends to develop near real-time estimates for the basic (R₀) and observed (R_obs) reproductive numbers associated with Zika virus disease in Colombia. We then validate our results against traditional health care-based disease surveillance data.

Methods

Cumulative reported case counts of Zika virus disease in Colombia were acquired via the HealthMap digital disease surveillance system. Linear smoothing was conducted to adjust the shape of the HealthMap cumulative case curve using Google search data. Traditional surveillance data on Zika virus disease were obtained from weekly Instituto Nacional de Salud (INS) epidemiological bulletin publications. The Incidence Decay and Exponential Adjustment (IDEA) model was used to estimate R₀ and R_obs for both data sources.

Results

Using the digital (smoothed HealthMap) data, we estimated a mean R₀ of 2.56 (range 1.42-3.83) and a mean R_obs of 1.80 (range 1.42-2.30). The traditional (INS) data yielded a mean R₀ of 4.82 (range 2.34-8.32) and a mean R_obs of 2.34 (range 1.60-3.31).

Conclusions

Although modeling using the traditional (INS) data yielded higher R₀ estimates than the digital (smoothed HealthMap) data, modeled ranges for R_obs were comparable across both data sources. As a result, the narrow range of possible case projections generated by the traditional (INS) data was largely encompassed by the wider range produced by the digital (smoothed HealthMap) data. Thus, in the absence of traditional surveillance data, digital surveillance data can yield similar estimates for key transmission parameters and should be utilized in other Zika virus-affected countries to assess outbreak dynamics in near real time.

Co-distribution and co-infection of chikungunya and dengue viruses

Background

Chikungunya and dengue infections are spatio-temporally related. The current review aims to determine the geographic limits of chikungunya, dengue and the principal mosquito vectors for both viruses and to synthesise current epidemiological understanding of their co-distribution.

Methods

Three biomedical databases (PubMed, Scopus and Web of Science) were searched from their inception until May 2015 for studies that reported concurrent detection of chikungunya and dengue viruses in the same patient. Additionally, data from WHO, CDC and Healthmap alerts were extracted to create up-to-date global distribution maps for both dengue and chikungunya.

Results

Evidence for chikungunya-dengue co-infection has been found in Angola, Gabon, India, Madagascar, Malaysia, Myanmar, Nigeria, Saint Martin, Singapore, Sri Lanka, Tanzania, Thailand and Yemen; these constitute only 13 out of the 98 countries/territories where both chikungunya and dengue epidemic/endemic transmission have been reported.

Conclusions

Understanding the true extent of chikungunya-dengue co-infection is hampered by current diagnosis largely based on their similar symptoms. Heightened awareness of chikungunya among the public and public health practitioners in the advent of the ongoing outbreak in the Americas can be expected to improve diagnostic rigour. Maps generated from the newly compiled lists of the geographic distribution of both pathogens and vectors represent the current geographical limits of chikungunya and dengue, as well as the countries/territories at risk of future incursion by both viruses. These describe regions of co-endemicity in which lab-based diagnosis of suspected cases is of higher priority.

Electronic supplementary material

The online version of this article (doi:10.1186/s12879-016-1417-2) contains supplementary material, which is available to authorized users.

Current issues in the economics of vaccination against dengue

Dengue is a major public health concern in tropical and subtropical areas of the world. The prospects for dengue prevention have recently improved with the results of efficacy trials of a tetravalent dengue vaccine. Although partially effective, once licensed, its introduction can be a public health priority in heavily affected countries because of the perceived public health importance of dengue. This review explores the most immediate economic considerations of introducing a new dengue vaccine and evaluates the published economic analyses of dengue vaccination. Findings indicate that the current economic evidence base is of limited utility to support country-level decisions on dengue vaccine introduction. There are a handful of published cost-effectiveness studies and no country-specific costing studies to project the full resource requirements of dengue vaccine introduction. Country-level analytical expertise in economic analyses, another gap identified, needs to be strengthened to facilitate evidence-based decision-making on dengue vaccine introduction in endemic countries.

Cost-utility analysis of dengue vaccination in a country with heterogeneous risk of dengue transmission

BACKGROUND

Dengue is one of the most important vector-borne diseases worldwide, and annually, nearly 390 million people are infected and 500,000 patients are hospitalized for severe dengue. Argentina has great variability in the risk of dengue transmission due to eco-climatic reasons. Currently no vaccines are available for dengue even though several vaccines are under development.

OBJECTIVE

The aim of this study was to estimate the cost-effectiveness of a dengue vaccine in a country with heterogeneous risk of dengue transmission like Argentina.

METHODS

The analysis was carried out from a societal perspective using a Markov model that included both vaccine and disease parameters. Utility was measured as disability adjusted life years (DALYs) averted, and the incremental cost-effectiveness ratio (ICER) of the vaccination was expressed in 2014 American dollars (US$) per DALY averted. One-way and probabilistic sensitivity analyses were performed to evaluate uncertainty in model outcomes, and a threshold analysis was conducted to estimate the highest possible price of the vaccine.

RESULTS

The ICER of the vaccination program was found to be US$ 5714 per DALY averted. This value is lower than 3 times the per capita GDP of Argentina (US$ 38,619 in 2014); 54.9% of the simulations were below this value. If a vaccination program would be implemented the maximum vaccine price per dose has to be US$1.49 for a vaccination at national level or US$28.72 for a targeted vaccination in high transmission areas.

CONCLUSIONS

These results demonstrate that vaccination against dengue would be cost-effective in Argentina, especially if carried out in predetermined regions at high risk of dengue transmission. However, these results should be interpreted with caution because the probabilistic sensitivity analysis showed that there was considerable uncertainty around the ICER value. The influence of variations in vaccine efficacy, cost and other important parameters are discussed in the text.

Predicting Wolbachia potential to knock down dengue virus transmission

Releasing mosquitoes infected with the intracellular bacteria Wolbachia is a candidate strategy for dengue control that has recently advanced to field-testing. A critical next step is to evaluate the impact of this strategy on dengue epidemiology. A recent study by Ferguson and colleagues presents a mathematical framework to predict the likely effect of mosquitoes carrying Wolbachia on dengue virus transmission. Fitting the mathematical model to empirical data obtained with Wolbachia-infected mosquitoes experimentally challenged with viremic blood from dengue patients indicates that dengue virus transmission could be reduced by a degree that would have a significant impact on public health.

Modeling the impact on virus transmission of Wolbachia-mediated blocking of dengue virus infection of Aedes aegypti

Dengue is the most common arboviral infection of humans and is a public health burden in more than 100 countries. Aedes aegypti mosquitoes stably infected with strains of the intracellular bacterium Wolbachia are resistant to dengue virus (DENV) infection and are being tested in field trials. To mimic field conditions, we experimentally assessed the vector competence of A. aegypti carrying the Wolbachia strains wMel and wMelPop after challenge with viremic blood from dengue patients. We found that wMelPop conferred strong resistance to DENV infection of mosquito abdomen tissue and largely prevented disseminated infection. wMel conferred less resistance to infection of mosquito abdomen tissue, but it did reduce the prevalence of mosquitoes with infectious saliva. A mathematical model of DENV transmission incorporating the dynamics of viral infection in humans and mosquitoes was fitted to the data collected. Model predictions suggested that wMel would reduce the basic reproduction number, R0, of DENV transmission by 66 to 75%. Our results suggest that establishment of wMelPop-infected A. aegypti at a high frequency in a dengue-endemic setting would result in the complete abatement of DENV transmission. Establishment of wMel-infected A. aegypti is also predicted to have a substantial effect on transmission that would be sufficient to eliminate dengue in low or moderate transmission settings but may be insufficient to achieve complete control in settings where R0 is high. These findings develop a framework for selecting Wolbachia strains for field releases and for calculating their likely impact.

Theory and data for simulating fine-scale human movement in an urban environment

Individual-based models of infectious disease transmission depend on accurate quantification of fine-scale patterns of human movement. Existing models of movement either pertain to overly coarse scales, simulate some aspects of movement but not others, or were designed specifically for populations in developed countries. Here, we propose a generalizable framework for simulating the locations that an individual visits, time allocation across those locations, and population-level variation therein. As a case study, we fit alternative models for each of five aspects of movement (number, distance from home and types of locations visited; frequency and duration of visits) to interview data from 157 residents of the city of Iquitos, Peru. Comparison of alternative models showed that location type and distance from home were significant determinants of the locations that individuals visited and how much time they spent there. We also found that for most locations, residents of two neighbourhoods displayed indistinguishable preferences for visiting locations at various distances, despite differing distributions of locations around those neighbourhoods. Finally, simulated patterns of time allocation matched the interview data in a number of ways, suggesting that our framework constitutes a sound basis for simulating fine-scale movement and for investigating factors that influence it.

Meteorologically Driven Simulations of Dengue Epidemics in San Juan, PR

Meteorological factors influence dengue virus ecology by modulating vector mosquito population dynamics, viral replication, and transmission. Dynamic modeling techniques can be used to examine how interactions among meteorological variables, vectors and the dengue virus influence transmission. We developed a dengue fever simulation model by coupling a dynamic simulation model for Aedes aegypti, the primary mosquito vector for dengue, with a basic epidemiological Susceptible-Exposed-Infectious-Recovered (SEIR) model. Employing a Monte Carlo approach, we simulated dengue transmission during the period of 2010-2013 in San Juan, PR, where dengue fever is endemic. The results of 9600 simulations using varied model parameters were evaluated by statistical comparison (r2) with surveillance data of dengue cases reported to the Centers for Disease Control and Prevention. To identify the most influential parameters associated with dengue virus transmission for each period the top 1% of best-fit model simulations were retained and compared. Using the top simulations, dengue cases were simulated well for 2010 (r2 = 0.90, p = 0.03), 2011 (r2 = 0.83, p = 0.05), and 2012 (r2 = 0.94, p = 0.01); however, simulations were weaker for 2013 (r2 = 0.25, p = 0.25) and the entire four-year period (r2 = 0.44, p = 0.002). Analysis of parameter values from retained simulations revealed that rain dependent container habitats were more prevalent in best-fitting simulations during the wetter 2010 and 2011 years, while human managed (i.e. manually filled) container habitats were more prevalent in best-fitting simulations during the drier 2012 and 2013 years. The simulations further indicate that rainfall strongly modulates the timing of dengue (e.g., epidemics occurred earlier during rainy years) while temperature modulates the annual number of dengue fever cases. Our results suggest that meteorological factors have a time-variable influence on dengue transmission relative to other important environmental and human factors.

The Hidden Burden of Dengue and Chikungunya in Chennai, India

Background

Dengue and chikungunya are rapidly expanding viruses transmitted by mosquitoes of the genus Aedes. Few epidemiological studies have examined the extent of transmission of these infections in South India despite an increase in the number of reported cases, and a high suitability for transmission.

Methods and findings

We conducted a household-based seroprevalence survey among 1010 individuals aged 5-40 years living in fifty randomly selected spatial locations in Chennai, Tamil Nadu. Participants were asked to provide a venous blood sample and to complete a brief questionnaire with basic demographic and daily activity information. Previous exposure to dengue and chikungunya was determined using IgG indirect ELISA (Panbio) and IgG ELISA (Novatec), respectively. We used this data to estimate key transmission parameters (force of infection and basic reproductive number) and to explore factors associated with seropositivity. While only 1% of participants reported history of dengue and 20% of chikungunya, we found that 93% (95%CI 89-95%) of participants were seropositive to dengue virus, and 44% (95%CI 37-50%) to chikungunya. Age-specific seroprevalence was consistent with long-tem, endemic circulation of dengue and suggestive of epidemic chikungunya transmission. Seropositivity to dengue and chikungunya were significantly correlated, even after adjusting for individual and household factors. We estimate that 23% of the susceptible population gets infected by dengue each year, corresponding to approximately 228,000 infections. This transmission intensity is significantly higher than that estimated in known hyperendemic settings in Southeast Asia and the Americas.

Conclusions

These results provide unprecedented insight into the very high transmission potential of dengue and chikungunya in Chennai and underscore the need for enhanced surveillance and control methods.

Despite a recent increase in the number of cases, little data exist on the extent of dengue and chikungunya transmission in Indian cities. We conducted a household-based serosurvey conducted in randomly selected spatial locations across the metropolis of Chennai. We tested samples for evidence of previous infection by dengue and chikungunya viruses and used this data to estimate key transmission parameters (force of infection and basic reproductive number) and to explore factors associated with seropositivity. We found that 93% of participants had been exposed to dengue virus, and 44% to chikungunya. We estimate that 23% of the susceptible population gets infected by dengue virus each year, corresponding to approximately 228,000 infections per year. This transmission intensity is almost three times larger than that in traditionally hyperendemic district in Thailand, and suggests an extremely large proportion of asymptomatic/sub-clinical disease, a lack of recognition of the disease and/or under-reporting.

A critical assessment of vector control for dengue prevention

Recently, the Vaccines to Vaccinate (v2V) initiative was reconfigured into the Partnership for Dengue Control (PDC), a multi-sponsored and independent initiative. This redirection is consistent with the growing consensus among the dengue-prevention community that no single intervention will be sufficient to control dengue disease. The PDC's expectation is that when an effective dengue virus (DENV) vaccine is commercially available, the public health community will continue to rely on vector control because the two strategies complement and enhance one another. Although the concept of integrated intervention for dengue prevention is gaining increasingly broader acceptance, to date, no consensus has been reached regarding the details of how and what combination of approaches can be most effectively implemented to manage disease. To fill that gap, the PDC proposed a three step process: (1) a critical assessment of current vector control tools and those under development, (2) outlining a research agenda for determining, in a definitive way, what existing tools work best, and (3) determining how to combine the best vector control options, which have systematically been defined in this process, with DENV vaccines. To address the first step, the PDC convened a meeting of international experts during November 2013 in Washington, DC, to critically assess existing vector control interventions and tools under development. This report summarizes those deliberations.

Estimating drivers of autochthonous transmission of chikungunya virus in its invasion of the americas

Background Chikungunya is an emerging arbovirus that has caused explosive outbreaks in Africa and Asia for decades and invaded the Americas just over a year ago. During this ongoing invasion, it has spread to 45 countries where it has been transmitted autochthonously, infecting nearly 1.3 million people in total. Methods Here, we made use of weekly, country-level case reports to infer relationships between transmission and two putative climatic drivers: temperature and precipitation averaged across each country on a monthly basis. To do so, we used a TSIR model that enabled us to infer a parametric relationship between climatic drivers and transmission potential, and we applied a new method for incorporating a probabilistic description of the serial interval distribution into the TSIR framework. Results We found significant relationships between transmission and linear and quadratic terms for temperature and precipitation and a linear term for log incidence during the previous pathogen generation. The lattermost suggests that case numbers three to four weeks ago are largely predictive of current case numbers. This effect is quite nonlinear at the country level, however, due to an estimated mixing parameter of 0.74. Relationships between transmission and the climatic variables that we estimated were biologically plausible and in line with expectations. Conclusions Our analysis suggests that autochthonous transmission of Chikungunya in the Americas can be correlated successfully with putative climatic drivers, even at the coarse scale of countries and using long-term average climate data. Overall, this provides a preliminary suggestion that successfully forecasting the future trajectory of a Chikungunya outbreak and the receptivity of virgin areas may be possible. Our results also provide tentative estimates of timeframes and areas of greatest risk, and our extension of the TSIR model provides a novel tool for modeling vector-borne disease transmission.

Dengue in children

Dengue is a mosquito-borne viral disease of expanding geographical range and increasing incidence. The vast majority of dengue cases are children less than 15 years of age. Dengue causes a spectrum of illness from mild fever to severe disease with plasma leakage and shock. Infants and children with secondary heterologous dengue infections are most at risk for severe dengue disease. Laboratory diagnosis of dengue can be established within five days of disease onset by direct detection of viral components in serum. After day five, serologic diagnosis provides indirect evidence of dengue. Currently, no effective antiviral agents are available to treat dengue infection. Therefore, treatment remains supportive, with emphasis on close hematological monitoring, recognition of warning signs of severe disease and fluid-replacement therapy and/or blood transfusions when required. Development of a dengue vaccine is considered a high public health priority. A safe and efficacious dengue vaccine would also be important for travelers. This review highlights the current understanding of dengue in children, including its clinical manifestations, pathogenesis, diagnostic tests, management and prevention.

The 2012 Madeira dengue outbreak: epidemiological determinants and future epidemic potential

Dengue, a vector-borne viral disease of increasing global importance, is classically associated with tropical and sub-tropical regions around the world. Urbanisation, globalisation and climate trends, however, are facilitating the geographic spread of its mosquito vectors, thereby increasing the risk of the virus establishing itself in previously unaffected areas and causing large-scale epidemics. On 3 October 2012, two autochthonous dengue infections were reported within the Autonomous Region of Madeira, Portugal. During the following seven months, this first 'European' dengue outbreak caused more than 2000 local cases and 81 exported cases to mainland Europe. Here, using an ento-epidemiological mathematical framework, we estimate that the introduction of dengue to Madeira occurred around a month before the first official cases, during the period of maximum influx of airline travel, and that the naturally declining temperatures of autumn were the determining factor for the outbreak's demise in early December 2012. Using key estimates, together with local climate data, we further propose that there is little support for dengue endemicity on this island, but a high potential for future epidemic outbreaks when seeded between May and August-a period when detection of imported cases is crucial for Madeira's public health planning.

Time-varying, serotype-specific force of infection of dengue virus

Infectious disease models play a key role in public health planning. These models rely on accurate estimates of key transmission parameters such as the force of infection (FoI), which is the per-capita risk of a susceptible person being infected. The FoI captures the fundamental dynamics of transmission and is crucial for gauging control efforts, such as identifying vaccination targets. Dengue virus (DENV) is a mosquito-borne, multiserotype pathogen that currently infects ∼390 million people a year. Existing estimates of the DENV FoI are inaccurate because they rely on the unrealistic assumption that risk is constant over time. Dengue models are thus unreliable for designing vaccine deployment strategies. Here, we present to our knowledge the first time-varying (daily), serotype-specific estimates of DENV FoIs using a spline-based fitting procedure designed to examine a 12-y, longitudinal DENV serological dataset from Iquitos, Peru (11,703 individuals, 38,416 samples, and 22,301 serotype-specific DENV infections from 1999 to 2010). The yearly DENV FoI varied markedly across time and serotypes (0-0.33), as did daily basic reproductive numbers (0.49-4.72). During specific time periods, the FoI fluctuations correlated across serotypes, indicating that different DENV serotypes shared common transmission drivers. The marked variation in transmission intensity that we detected indicates that intervention targets based on one-time estimates of the FoI could underestimate the level of effort needed to prevent disease. Our description of dengue virus transmission dynamics is unprecedented in detail, providing a basis for understanding the persistence of this rapidly emerging pathogen and improving disease prevention programs.