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![a. Reported DHF incidence from 1972 to 2007 (per 100,000 inhabitants). b. Reported dengue incidence for <15 years old and 15+ years old individual per level of severity (per 100,000 inhabitants) c. based on reported incidence and serotype distribution of isolated virus (about 1% of reported cases are subject to virus isolation) d. Seasonality in vector (Aedes Aegypti) density and monthly DHF incidence. Seasonality was assessed through locally weighted scatterplot smoothing [61]. Data derived from the surveillance system of dengue in Southern Vietnam coordinated by the Pasteur Institute in Ho Chi Minh.](profile/Laurent-Coudeville/publication/233950105/figure/fig2/AS:214248312578049@1428092186641/a-Reported-DHF-incidence-from-1972-to-2007-per-100-000-inhabitants-b-Reported-dengue.png)
a. Reported DHF incidence from 1972 to 2007 (per 100,000 inhabitants). b. Reported dengue incidence for <15 years old and 15+ years old individual per level of severity (per 100,000 inhabitants) c. based on reported incidence and serotype distribution of isolated virus (about 1% of reported cases are subject to virus isolation) d. Seasonality in vector (Aedes Aegypti) density and monthly DHF incidence. Seasonality was assessed through locally weighted scatterplot smoothing [61]. Data derived from the surveillance system of dengue in Southern Vietnam coordinated by the Pasteur Institute in Ho Chi Minh.
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With approximately 2.5 billion people at risk, dengue is a major international public health concern. Dengue vaccines currently in development should help reduce the burden associated with this disease but the most efficient way of using future dengue vaccines remains to be defined. Mathematical models of transmission can provide insight into the e...
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Many infectious diseases are not maintained in a state of equilibrium but exhibit significant fluctuations in prevalence over time. For pathogens that consist of multiple antigenic types or strains, such as influenza, malaria or dengue, these fluctuations often take on the form of regular or irregular epidemic outbreaks in addition to oscillatory p...
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... Laboratory procedures to identify pathogen subtypes (e.g., with respect to strain, genotype, serotype, variant, or phenotypic traits such as drug resistance) are important components of infectious disease surveillance, yielding information on transmissibility, clinical spectrum, evolutionary trends, and subtype-specific risk factors [1][2][3][4][5][6][7]. Indeed, information gathered from laboratory pathogen typing is integral to modern disease surveillance, enabling the discovery of SARS-CoV-2 variants with higher transmissibility [7], influenza A serotypes with high mortality and transmissibility [5], changes in the prevalence rate of drug-resistant tuberculosis and Methicillin-resistant Staphylococcus aureus (MRSA) [8,9], shifts in dominant serotypes causing invasive pneumococcal disease [6], and differing routes of infection across hepatitis C virus genotypes [10]. ...
With the aid of laboratory typing techniques, infectious disease surveillance networks have the opportunity to obtain powerful information on the emergence, circulation, and evolution of multiple genotypes, serotypes or other subtypes of pathogens, informing understanding of transmission dynamics and strategies for prevention and control. The volume of typing performed on clinical isolates is typically limited by its ability to inform clinical care, cost and logistical constraints, especially in comparison with the capacity to monitor clinical reports of disease occurrence, which remains the most widespread form of public health surveillance. Viewing clinical disease reports as arising from a latent mixture of pathogen subtypes, laboratory typing of a subset of clinical cases can provide inference on the proportion of clinical cases attributable to each subtype (i.e., the mixture components). Optimizing protocols for the selection of isolates for typing by weighting specific subpopulations, locations, time periods, or case characteristics (e.g., disease severity), may improve inference of the frequency and distribution of pathogen subtypes within and between populations. Here, we apply the Disease Surveillance Informatics Optimization and Simulation (DIOS) framework to simulate and optimize hand foot and mouth disease (HFMD) surveillance in a high-burden region of western China. We identify laboratory surveillance designs that significantly outperform the existing network: the optimal network reduced mean absolute error in estimated serotype-specific incidence rates by 14.1%; similarly, the optimal network for monitoring severe cases reduced mean absolute error in serotype-specific incidence rates by 13.3%. In both cases, the optimal network designs achieved improved inference without increasing subtyping effort. We demonstrate how the DIOS framework can be used to optimize surveillance networks by augmenting clinical diagnostic data with limited laboratory typing resources, while adapting to specific, local surveillance objectives and constraints.
... Billings et al. [3] analysed an ODE model to determine the impact of single-strain vaccine campaigns on the epidemic multistrain model dynamics. Coudeville and Garnett [8] studied the effect of vaccination on the age-structured, serotype-specific compartment model for dengue disease. Kar and Jana [20] analysed the combined control strategies on both host (treatment and vaccination controls) and vector (insecticide control). ...
In this paper, a non-linear mathematical model is proposed and analyzed to study the role of vaccination and control
measures on the spread of vector-borne diseases. It is assumed that susceptible hosts can be infected either directly or
indirectly. In the modelling process, it is considered that only a susceptible person can be vaccinated. The existence
of the control problem is proved and later used to investigate effective control efforts for the prevention of direct and
indirect transmission of disease. The model is analyzed using Hurwitz and Sylvester’s criterion. The analysis of the model reveals that, if the vaccination reproduction number Rv is less than one, the disease can be eradicated provided,
and the vaccine is highly efficient
... General symptoms of dengue fever (DF) include acute high fevers, headache, pains around the eye socket and muscle pains. Dengue hemorrhagic fever (DHF) may have more severe symptoms which are nosebleeds, vomiting blood, and plasma leakage and in some cases could include hypotension, anuria, and shock, which is called dengue shock syndrome (DSS) [5,6]. Infection by one strain of the dengue virus provides immunity to further infection by that strain but not to the others. ...
The effect of vaccination on the dengue fever epidemic described by an age structured modified SIR (Susceptible-Infected-Retired) model is studied using standard stability analysis. The chimeric yellow fever dengue tetravalent dengue vaccine (CYD-TDV™) is a vaccine recently developed to control this epidemic in several Southeast Asian countries. The dengue vaccination program requires a total of three injections, 6 months apart at 0, 6, and 12 months. The ages of the recipients are nine years and above. In this paper, we analyze the mathematical dynamics SIR transmission model of the epidemic. The stability of the model is established using Routh–Hurwitz criteria to see if a Hopf Bifurcation occurs and see when the equilibrium states are local asymptotically stable or global asymptotically stable. We have determined the efficiency of CYD-TDV by simulating the optimal numerical solution for each age range for this model. The numerical results showed the optimal age for vaccination and significantly reduced the severity and severity of the disease.
... Another complex model was proposed by Coudeville and Garnett [91]. An age-structured, vector-host and serotype-specific compartmental model, including seasonality, was developed and analyzed. ...
... By adding vaccine dynamics to their model, they show that the overall impact of vaccination depends on the efficacy and the duration of protection that the vaccine confers. Similar to the model proposed in [91], Knerer and colleagues [85] have developed a model featuring seasonality, age-structure, and consecutive infection by all four serotypes of dengue. The model was able to describe the national data from Thailand when incorporating seasonality and TCI between serotypes. ...
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 analysed.
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.
... Some of these include population renewal [22] and spatio-temporal heterogeneity which may lead to source-sink dynamics on the national [23] and regional scale [15]. Vector dynamics and immunological interactions [4,24] are also posited as drivers of multi-annual cycles of outbreaks. Our analysis explicitly revealed the persistence of dengue transmission for each specific serotype, as well as the persistence of across serotype dynamics. ...
Over 105 million dengue infections are estimated to occur annually. Understanding the disease dynamics of dengue is often difficult due to multiple strains circulating within a population. Interactions between dengue serotype dynamics may result in complex cross-immunity dynamics at the population level and create difficulties in terms of formulating intervention strategies for the disease. In this study, a nationally representative 16-year time series with over 43 000 serotyped dengue infections was used to infer the long-run effects of between and within strain interactions and their impacts on past outbreaks. We used a novel identification strategy incorporating sign-identified Bayesian vector autoregressions, using structural impulse responses, historical decompositions and counterfactual analysis to conduct inference on dengue dynamics post-estimation. We found that on the population level: (i) across-serotype interactions on the population level were highly persistent, with a one time increase in any other serotype associated with long run decreases in the serotype of interest (range: 0.5–2.5 years) and (ii) over 38.7% of dengue cases of any serotype were associated with across-serotype interactions. The findings in this paper will substantially impact public health policy interventions with respect to dengue.
... It is well known that the period for individuals in latent compartment is different from one to one, which depends on individuals situation. For dengue fever, the period for individuals in latent compartment varies from 3 to 14 days and its distributions usually peak around their mean [3,7]. And for tuberculosis, the latent period for individuals in latent compartment may take months, years or even decades. ...
... They also showed that the introduction of age structure may change the dynamics of the corresponding model without age structure. Additionally, recent studies [3,15] pointed out cross immunity starts immediately after the primary infectious period and prevents individuals from becoming infected by another strain for a period ranging from 6 months to 9 months, even to lifelong. To the best of our knowledge, there is currently no work on the effect of cross immunity age on the dynamics of dengue fever model. ...
Dengue fever is a typical mosquito-borne infectious disease, and four strains of it are currently found. Clinical medical research has shown that the infected person can provide life-long immunity against the strain after recovering from infection with one strain, but only provide partial and temporary immunity against other strains. On the basis of the complexity of transmission and the diversity of pathogens, in this paper, a multi-strain dengue transmission model with latency age and cross immunity age is proposed. We discuss the well-posedness of this model and give the terms of the basic reproduction number R0 = max{R1, R2 }, where Ri is the basic reproduction number of strain i (i = 1, 2). Particularly, we obtain that the model always has a unique disease-free equilibrium P0 which is locally stable for R0 < 1. And same time, an explicit condition of the global asymptotic stability of P0 is obtained by constructing a suitable Lyapunov functional. Furthermore, we also shown that if Ri > 1, the strain-i dominant equilibrium Pi is locally stable for Rj < R∗i (i, j = 1, 2, i ̸= j). Additionally, the threshold criteria on the uniformly persistence, the existence and global asymptotically stability of coexistence equilibrium are also obtained. Finally, these theoretical results and interesting conclusions are illustrated with some numerical simulations.
... It is an interesting question whether this is still caused by the aftermath of the 2014 outbreak and will vanish in the next few years or whether dengue has to be considered endemic in China again. Future modelling studies could include different dengue serotypes [69] and thus more realistically predict severe dengue cases with potential cross reactions. Finally, future studies could also introduce stochasticity in mosquito parameters or regional habitat qualities to further assess model uncertainty [70,71]. ...
Dengue is considered non-endemic to mainland China. However, travellers frequently import the virus from overseas and local mosquito species can then spread the disease in the population. As a consequence, mainland China still experiences large dengue outbreaks. Temperature plays a key role in these outbreaks: it affects the development and survival of the vector and the replication rate of the virus. To better understand its implication in the transmission risk of dengue, we developed a delay differential equation model that explicitly simulates temperature-dependent development periods and tested it with collected field data for the Asian tiger mosquito, Aedes albopictus . The model predicts mosquito occurrence locations with a high accuracy (Cohen’s κ of 0.78) and realistically replicates mosquito population dynamics. Analysing the infection dynamics during the 2014 dengue outbreak that occurred in Guangzhou showed that the outbreak could have lasted for another four weeks if mosquito control interventions had not been undertaken. Finally, we analyse the dengue transmission risk in mainland China. We find that southern China, including Guangzhou, can have more than seven months of dengue transmission per year while even Beijing, in the temperate north, can have dengue transmission during hot summer months. The results demonstrate the importance of using detailed vector and infection ecology, especially when vector-borne disease transmission risk is modelled over a broad range of climatic zones.
... The population dynamics of Dengue serotypes with direct transmission has been studied [4,42,37] including the role of vector dynamics [49,15]. Some models have incorporated the effects on disease prevalence and morbidities of serotype similarities and cross-reactions in the host [33,49,49,2,1,51]. ...
The characterization of dengue serotypes to account for phenomena observed in its population dynamics, has been largely done in terms of cross-reactivity that includes cross-protection and cross-enhancement. The four dengue serotypes (DENV1-DENV4) are generally classified by their genetic and antigenic properties. Studies report that DENV2 and DENV4 are the most genetically distant serotypes. Reports on antigenic similarity, on the other hand, indicate that DENV1 and DENV2 are the most distant. While sequences of infections most frequently associated to severe dengue are DENV1/DENV2 and DENV1/DENV3. These observations along and the observed heterogeneity in serotype transmission motivate our question as to how to design a mathematical model that incorporates both the history of infections and the cross reactions that affect host susceptibility. We explore population dynamics of infections by the four serotypes based on genetic or antigenic similarity and host susceptibility of individuals with a history of successive infections. We define a mathematical framework to represent host-susceptibility triggered by serotype cross-reactions (cross-protection and cross-enhancement) that account for serotype distance-based (genetic and antigenic) interactions. This approach is achieved by the introduction of what we call a distance-based Reinfection Matrix from which we can also define a serotype hierarchy. We find that both, immune cross-enhancement and immune cross-protection can promote alternating patterns in serotype dynamics. An interplay between serotype Reinfection hierarchy and transmission rates appears to be associated with serotype incidence amount. Simulations suggest that the more similar the serotypes are, the longer the interoutbreak period between them is.
... Mathematical models of dengue fever have been developed to gain insights into disease transmission [12,109,246,254,257,261,[279][280][281], predict outbreaks as well as simulate the impact of interventions for disease control [247,250,277,[282][283][284][285][286][287][288][289][290][291]. Historically, studies tend to be divided into those that consider mechanical and chemical interventions on the one hand [282,283,[285][286][287][288][289][290][291], and those that consider vaccination on the other [247,250,284]. ...
... Mathematical models of dengue fever have been developed to gain insights into disease transmission [12,109,246,254,257,261,[279][280][281], predict outbreaks as well as simulate the impact of interventions for disease control [247,250,277,[282][283][284][285][286][287][288][289][290][291]. Historically, studies tend to be divided into those that consider mechanical and chemical interventions on the one hand [282,283,[285][286][287][288][289][290][291], and those that consider vaccination on the other [247,250,284]. On the whole, few have begun to consider the combined effects of a range of different interventions including vaccination [292]. ...
... The authors indicated that mosquito control, by itself, would have increased the incidences of DF and DHF (as a result of age-related disease manifestation or some form of immunological mechanism) in areas of high mosquito density and that, to combat this, mosquito-control programmes should only be carried out after a vaccination programme with high coverage has been initiated [427]. Similarly to Coudeville and Garnett [250], the authors did not model any specific interventions, rather vector control was also introduced by an arbitrary reduction in the vector population size, with the impact assessed in the basic reproduction number [427]. ...
Dengue fever has become a major public health problem. It is considered one of the most important mosquito-borne viral diseases and occurs in >100 countries in tropical and subtropical regions of Asia-Pacific, the Americas, the Middle East, and Africa with >3 billion people at risk. Despite current control interventions against dengue fever in endemic countries, the disease is associated with considerable healthcare utilisation, personal costs to patients and caregivers, productivity loss, and human suffering. Whilst the illness is well understood, there is also recognition that current control efforts focussing predominantly on Aedes aegypti control and elimination are less than optimal, although they may still have an important role to play in the short to medium term. In this thesis, the epidemiological and economic impacts of dengue control interventions in Thailand, a geographical setting with a persistent, high level of dengue transmission areinvestigated, embracing chemical interventions (adulticide and larvicide), environmental control/ public health education and awareness, paediatric vaccination (using dengue vaccine profile[s] broadly consistent with [dengue] vaccines in late-stage development) and, in anticipation of possible new vector-control technologies, Wolbachia-infected mosquitoes. The premise that is being examined is not the ‘how’ of implementation, rather what the possible population impacts of different interventions are (both individually and in combination). Using three different and complementary analyses (epidemiological impact, i.e. effectiveness, cost-effectiveness analysis, and constrained optimisation), our findings show that the most useful method to reduce dengue burden (in terms of cases, costeffectiveness, and affordability, respectively) would be to combine vaccination with other form(s) of control, e.g. adulticide, environmental control/ public health education and awareness, and Wolbachia.</i
... Louis et al. (2014) reviewed 26 published papers on mapping dengue risk and found that age and income are 2 of the most commonly used factors for predicting dengue risk. This is in line with the reported dengue incidence in the MDR showing a high rate of patients aged less than 15 years (Coudeville and Garnett, 2012). Alternatively, poverty contributes to reducing resilience to dengue, since poor people in developing countries appear to spend most of their income on food and housing rather than to health care services (Khun and Manderson, 2008). ...
Dengue fever has continuously been a disease burden in Vietnam during the last 20 years, particularly in the Mekong Delta region (MDR), which is one of the most vulnerable to climate change. Variations in temperature and precipitation are likely to alter the incidence and distribution of vector-borne diseases such as dengue. This study focuses on assessing dengue risk via the vulnerability concept, which is composed of exposure and susceptibility using a combined approach of mapping and modelling for the MDR of Vietnam during the period between 2001 and 2016. Multisource remote sensing data from Global Satellite Mapping of Precipitation (GSMaP) and Moderate Resolution Imaging Spectrophotometer (MODIS) was used for presenting climate and environment variables in mapping and modelling vulnerability. Monthly and yearly maps of vulnerability to dengue in the MDR, produced for 15-year period, aided analysis of the temporal and spatial patterns of vulnerability to dengue in the study region and were used for constructing time-series modelling of vulnerability for the following year. The results showed that there is a clear seasonal variation in the vulnerability due to variability of the climate factor and its strong dispersion across the study region, with higher vulnerability in the scattered areas of urban and mixed horticulture land and lower vulnerability in areas covered by forest and bare soil lands. The Pearson's correlation was applied to evaluate the association between dengue rates and vulnerability values aggregated at the provincial level. Reasonable linear association, with correlation coefficients of 0.41–0.63, was found in two-thirds of the provinces. The predicted vulnerabilities to dengue during 2016 were comparable with the estimated values and trends for most provinces of the MDR. Our demonstrated approach with integrated geospatial data seems to be a promising tool in supporting the public health sector in assessing potential space and time of a subsequent increase in vulnerability to dengue, particularly in the context of climate change.
