Co-circulation and misdiagnosis led to underestimation of the 2015-2017 Zika epidemic in the Americas

Shifting patterns of dengue three years after Zika virus emergence in Brazil

In 2015, the Zika virus (ZIKV) emerged in Brazil, leading to widespread outbreaks in Latin America. Following this, many countries in these regions reported a significant drop in the circulation of dengue virus (DENV), which resurged in 2018-2019. We examine age-specific incidence data to investigate changes in DENV epidemiology before and after the emergence of ZIKV. We observe that incidence of DENV was concentrated in younger individuals during resurgence compared to 2013-2015. This trend was more pronounced in Brazilian states that had experienced larger ZIKV outbreaks. Using a mathematical model, we show that ZIKV-induced cross-protection alone, often invoked to explain DENV decline across Latin America, cannot explain the observed age-shift without also assuming some form of disease enhancement. Our results suggest that a sudden accumulation of population-level immunity to ZIKV could suppress DENV and reduce the mean age of DENV incidence via both protective and disease-enhancing interactions.

Model-based estimates of chikungunya epidemiological parameters and outbreak risk from varied data types

Assessing the factors responsible for differences in outbreak severity for the same pathogen is a challenging task, since outbreak data are often incomplete and may vary in type across outbreaks (e.g., daily case counts, serology, cases per household). We propose that outbreaks described with varied data types can be directly compared by using those data to estimate a common set of epidemiological parameters. To demonstrate this for chikungunya virus (CHIKV), we developed a realistic model of CHIKV transmission, along with a Bayesian inference method that accommodates any type of outbreak data that can be simulated. The inference method makes use of the fact that all data types arise from the same transmission process, which is simulated by the model. We applied these tools to data from three real-world outbreaks of CHIKV in Italy, Cambodia, and Bangladesh to estimate nine model parameters. We found that these populations differed in several parameters, including pre-existing immunity and house-to-house differences in mosquito activity. These differences resulted in posterior predictions of local CHIKV transmission risk that varied nearly fourfold: 16% in Italy, 28% in Cambodia, and 62% in Bangladesh. Our inference method and model can be applied to improve understanding of the epidemiology of CHIKV and other pathogens for which outbreaks are described with varied data types.

The detection of Japanese encephalitis virus in municipal wastewater during an acute disease outbreak

AIM

To demonstrate the capability of wastewater-based surveillance (WBS) as a tool for detecting potential cases of Japanese Encephalitis Virus (JEV) infection in the community.

METHODS AND RESULTS

In this study, we explore the potential of WBS to detect cases of JEV infection by leveraging from an established SARS-CoV-2 wastewater surveillance program. We describe the use of two reverse transcriptase quantitative polymerase chain reaction (RTqPCR) assays targeting JEV to screen archived samples from two wastewater treatment plants (WWTPs). JEV was detected in wastewater samples collected during a timeframe coinciding with a cluster of acute human encephalitis cases, alongside concurrent evidence of JEV detection in mosquito surveillance and the sentinel chicken programs within South Australia's Riverland and Murraylands regions.

CONCLUSIONS

Current surveillance measures for JEV encounter multiple constraints, which may miss the early stages of JEV circulation or fail to capture the full extent of transmission. The detection of JEV in wastewater during a disease outbreak highlights the potential WBS has as a complementary layer to existing monitoring efforts forming part of the One Health approach required for optimal disease response and control.

ARCA: the interactive database for arbovirus reported cases in the Americas

BACKGROUND

Accurate case report data are essential to understand arbovirus dynamics, including spread and evolution of arboviruses such as Zika, dengue and chikungunya viruses. Giving the multi-country nature of arbovirus epidemics in the Americas, these data are not often accessible or are reported at different time scales (weekly, monthly) from different sources.

RESULTS

We developed a publicly available and user-friendly database for arboviral case data in the Americas: ARCA. ARCA is a relational database that is hosted on the ARCA website. Users can interact with the database through the website by submitting queries through the website, which generates displays results and allows users to download these results in different, convenient file formats. Users can choose to view arboviral case data through a table which containscontaining the number of cases for a particular week, a plot, or through a map.

CONCLUSION

Our ARCA database is a useful tool for arboviral epidemiology research allowing for complex queries, data visualization, integration, and formatting.

A machine learning model to assess potential misdiagnosed dengue hospitalization

Dengue, like other arboviruses with broad clinical spectra, can easily be misdiagnosed as other infectious diseases due to the overlap of signs and symptoms. During large outbreaks, severe dengue cases have the potential to overwhelm the health care system and understanding the burden of dengue hospitalizations is therefore important to better allocate medical care and public health resources. A machine learning model that used data from the Brazilian public healthcare system database and the National Institute of Meteorology (INMET) was developed to estimate potential misdiagnosed dengue hospitalizations in Brazil. The data was modeled into a hospitalization level linked dataset. Then, Random Forest, Logistic Regression and Support Vector Machine algorithms were assessed. The algorithms were trained by dividing the dataset in training/test set and performing a cross validation to select the best hyperparameters in each algorithm tested. The evaluation was done based on accuracy, precision, recall, F1 score, sensitivity, and specificity. The best model developed was Random Forest with an accuracy of 85% on the final reviewed test. This model shows that 3.4% (13,608) of all hospitalizations in the public healthcare system from 2014 to 2020 could have been dengue misdiagnosed as other diseases. The model was helpful in finding potentially misdiagnosed dengue and might be a useful tool to help public health decision makers in planning resource allocation.

Anopheles albimanus is a Potential Alphavirus Vector in the Americas

Despite its ecological flexibility and geographical co-occurrence with human pathogens, little is known about the ability of Anopheles albimanus to transmit arboviruses. To address this gap, we challenged An. albimanus females with four alphaviruses and one flavivirus and monitored the progression of infections. We found this species is an efficient vector of the alphaviruses Mayaro virus, O'nyong-nyong virus, and Sindbis virus, although the latter two do not currently exist in its habitat range. An. albimanus was able to become infected with Chikungunya virus, but virus dissemination was rare (indicating the presence of a midgut escape barrier), and no mosquito transmitted. Mayaro virus rapidly established disseminated infections in An. albimanus females and was detected in the saliva of a substantial proportion of infected mosquitoes. Consistent with previous work in other anophelines, we find that An. albimanus is refractory to infection with flaviviruses, a phenotype that did not depend on midgut-specific barriers. Our work demonstrates that An. albimanus may be a vector of neglected emerging human pathogens and adds to recent evidence that anophelines are competent vectors for diverse arboviruses.

Expected endpoints from future chikungunya vaccine trial sites informed by serological data and modeling

In recent decades, there has been an increased interest in developing a vaccine for chikungunya. However, due to its unpredictable transmission, planning for a chikungunya vaccine trial is challenging. To inform decision making on the selection of sites for a vaccine efficacy trial, we developed a new framework for projecting the expected number of endpoint events at a given site. In this framework, we first accounted for population immunity using serological data collated from a systematic review and used it to estimate parameters related to the timing and size of past outbreaks, as predicted by an SIR transmission model. Then, we used that model to project the infection attack rate of a hypothetical future outbreak, in the event that one were to occur at the time of a future trial. This informed projections of how many endpoint events could be expected if a trial were to take place at that site. Our results suggest that some sites may have sufficient transmission potential and susceptibility to support future vaccine trials, in the event that an outbreak were to occur at those sites. In general, we conclude that sites that have experienced outbreaks within the past 10 years may be poorer targets for chikungunya vaccine efficacy trials in the near future. Our framework also generates projections of the numbers of endpoint events by age, which could inform study participant recruitment efforts.

Machine-Learning-Based Forecasting of Dengue Fever in Brazilian Cities Using Epidemiologic and Meteorological Variables

Dengue is a serious public health concern in Brazil and globally. In the absence of a universal vaccine or specific treatments, prevention relies on vector control and disease surveillance. Accurate and early forecasts can help reduce the spread of the disease. In this study, we developed a model for predicting monthly dengue cases in Brazilian cities 1 month ahead, using data from 2007-2019. We compared different machine learning algorithms and feature selection methods using epidemiologic and meteorological variables. We found that different models worked best in different cities, and a random forests model trained on monthly dengue cases performed best overall. It produced lower errors than a seasonal naive baseline model, gradient boosting regression, a feed-forward neural network, or support vector regression. For each city, we computed the mean absolute error between predictions and true monthly numbers of dengue cases on the test data set. The median error across all cities was 12.2 cases. This error was reduced to 11.9 when selecting the optimal combination of algorithm and input features for each city individually. Machine learning and especially decision tree ensemble models may contribute to dengue surveillance in Brazil, as they produce low out-of-sample prediction errors for a geographically diverse set of cities.

Monitoring human arboviral diseases through wastewater surveillance: Challenges, progress and future opportunities

Arboviral diseases are caused by a group of viruses spread by the bite of infected arthropods. Amongst these, dengue, Zika, west nile fever and yellow fever cause the greatest economic and social impact. Arboviral epidemics have increased in frequency, magnitude and geographical extent over the past decades and are expected to continue increasing with climate change and expanding urbanisation. Arboviral prevalence is largely underestimated, as most infections are asymptomatic, nevertheless existing surveillance systems are based on passive reporting of loosely defined clinical syndromes with infrequent laboratory confirmation. Wastewater-based surveillance (WBS), which has been demonstrated to be useful for monitoring diseases with significant asymptomatic populations including COVID19 and polio, could be a useful complement to arboviral surveillance. We review the current state of knowledge and identify key factors that affect the feasibility of monitoring arboviral diseases by WBS to include viral shedding loads by infected persons, the persistence of shed arboviruses and the efficiency of their recovery from sewage. We provide a simple model on the volume of wastewater that needs to be processed for detection of arboviruses, in face of lower arboviral shedding rates. In all, this review serves to reflect on the key challenges that need to be addressed and overcome for successful implementation of arboviral WBS.

A Chikungunya Virus Multiepitope Recombinant Protein Expressed from the Binary System Insect Cell/Recombinant Baculovirus Is Useful for Laboratorial Diagnosis of Chikungunya

Chikungunya virus (CHIKV) is an arbovirus currently distributed worldwide, causing a disease that shares clinical signs and symptoms with other illnesses, such as dengue and Zika and leading to a challenging clinical differential diagnosis. In Brazil, CHIKV emerged in 2014 with the simultaneous introduction of both Asian and East/Central/South African (ECSA) genotypes. Laboratorial diagnosis of CHIKV is mainly performed by molecular and serological assays, with the latter more widely used. Although many commercial kits are available, their costs are still high for many underdeveloped and developing countries where the virus circulates. Here we described the development and evaluation of a multi-epitope recombinant protein-based IgG-ELISA (MULTREC IgG-ELISA) test for the specific detection of anti-CHIKV antibodies in clinical samples, as an alternative approach for laboratorial diagnosis. The MULTREC IgG-ELISA showed 86.36% of sensitivity and 100% of specificity, and no cross-reactivity with other exanthematic diseases was observed. The recombinant protein was expressed from the binary system insect cell/baculovirus using the crystal-forming baculoviral protein polyhedrin as a carrier of the target recombinant protein to facilitate recovery. The crystals were at least 10 times smaller in size and had an amorphous shape when compared to the polyhedrin wild-type crystal. The assay uses a multi-epitope antigen, representing two replicates of 18 amino acid sequences from the E2 region and a sequence of 17 amino acids from the nsP3 region of CHIKV. The recombinant protein was highly expressed, easy to purify and has demonstrated its usefulness in confirming chikungunya exposure, indeed showing a good potential tool for epidemiological surveillance.

Congenital Zika syndrome and living conditions in the largest city of northeastern Brazil

Background

The Zika virus (ZIKV) epidemic hit Brazil in 2015 and resulted in a generation of children at risk of congenital Zika syndrome (CZS). The social vulnerability of certain segments of the population contributed to the disproportional occurrence of CZS in the Brazilian Northeast, the poorest region in the country. Living conditions are essential factors in understanding the social determination of CZS, which is embedded in a complex interaction between biological, environmental, and social factors. Salvador, the biggest city in the region, played a central role in the context of the epidemic and was a pioneer in reporting the ZIKV infection and registering a high number of cases of CZS. The aim of the study was identifying the incidence and spatial distribution pattern of children with CZS in the municipality of Salvador, according to living conditions.

Methods

This is an ecological study that uses the reported cases of ZIKV and CZS registered in the epidemiological surveillance database of the Municipal Secretariat of Health of the city of Salvador between August of 2015 and July of 2016. The neighborhoods formed the analysis units and the thematic maps were built based on the reported cases. Associations between CZS and living conditions were assessed using the Kernel ratio and a spatial autoregressive linear regression model.

Results

Seven hundred twenty-six live births were reported, of which 236 (32.5%) were confirmed for CZS. Despite the reports of ZIKV infection being widely distributed, the cases of CZS were concentrated in poor areas of the city. A positive spatial association was observed between living in places with poorer living conditions and births of children with CZS.

Conclusions

This study shows the role of living conditions in the occurrence of births of children with CZS and indicates the need for approaches that recognize the part played by social inequalities in determining CZS and in caring for the children affected.

A poisson-multinomial spatial model for simultaneous outbreaks with application to arboviral diseases

Dengue, Zika, and chikungunya are arboviral diseases (AVD) transmitted mainly by Aedes aegypti. Rio de Janeiro city, Brazil, has been endemic for dengue for over 30 years, and experienced the first joint epidemic of the three diseases between 2015-2016. They present similar symptoms and only a small proportion of cases are laboratory-confirmed. These facts lead to potential misdiagnosis and, consequently, uncertainty in the registration of the cases. We have available the number of cases of each disease for the n=160 neighborhoods of Rio de Janeiro. We propose a Poisson model for the total number of cases of Aedes-borne diseases and, conditioned on the total, we assume a multinomial model for the allocation of the number of cases of each of the diseases across the neighborhoods. This provides simultaneously the estimation of the associations of the relative risk of the total cases of AVD with environmental and socioeconomic variables; and the estimation of the probability of presence of each disease as a function of available covariates. Our findings suggest that a one standard deviation increase in the social development index decreases the relative risk of the total cases of AVD by 28%. Neighborhoods with smaller proportion of green area had greater odds of having chikungunya in comparison to dengue and Zika. A one standard deviation increase in population density decreases the odds of a neighborhood having Zika instead of dengue by 18% but increases the odds of chikungunya in comparison to dengue by 18% and by 43% in comparison to Zika.

Arboviral disease record data - Dengue and Chikungunya, Brazil, 2013-2020

One of the main categories of Neglected Tropical Diseases (NTDs) are arboviruses, of which Dengue and Chikungunya are the most common. Arboviruses mainly affect tropical countries. Brazil has the largest absolute number of cases in Latin America. This work presents a unified data set with clinical, sociodemographic, and laboratorial data on confirmed patients of Dengue and Chikungunya, as well as patients ruled out of infection from these diseases. The data is based on case notification data submitted to the Brazilian Information System for Notifiable Diseases, from Portuguese Sistema de Informação de Agravo de Notificação (SINAN), from 2013 to 2020. The original data set comprised 13,421,230 records and 118 attributes. Following a pre-processing process, a final data set of 7,632,542 records and 56 attributes was generated. The data presented in this work will assist researchers in investigating antecedents of arbovirus emergence and transmission more generally, and Dengue and Chikungunya in particular. Furthermore, it can be used to train and test machine learning models for differential diagnosis and multi-class classification.

Measurement(s)	clinical data
Technology Type(s)	interview

Tackling the global health threat of arboviruses: An appraisal of the three holistic approaches to health

Background: The rapid circulation of arboviruses in the human population has been linked with changes in climatic, environmental, and socio-economic conditions. These changes are known to alter the transmission cycles of arboviruses involving the anthropophilic vectors and thus facilitate an extensive geographical distribution of medically important arboviral diseases, thereby posing a significant health threat. Using our current understanding and assessment of relevant literature, this review aimed to understand the underlying factors promoting the spread of arboviruses and how the three most renowned interdisciplinary and holistic approaches to health such as One Health, Eco-Health, and Planetary Health can be a panacea for control of arboviruses. Methods: A comprehensive structured search of relevant databases such as Medline, PubMed, WHO, Scopus, Science Direct, DOAJ, AJOL, and Google Scholar was conducted to identify recent articles on arboviruses and holistic approaches to health using the keywords including "arboviral diseases", "arbovirus vectors", "arboviral infections", "epidemiology of arboviruses", "holistic approaches", "One Health", "Eco-Health", and "Planetary Health". Results: Changes in climatic factors like temperature, humidity, and precipitation support the growth, breeding, and fecundity of arthropod vectors transmitting the arboviral diseases. Increased human migration and urbanization due to socio-economic factors play an important role in population increase leading to the rapid geographical distribution of arthropod vectors and transmission of arboviral diseases. Medical factors like misdiagnosis and misclassification also contribute to the spread of arboviruses. Conclusion: This review highlights two important findings: First, climatic, environmental, socio-economic, and medical factors influence the constant distributions of arthropod vectors. Second, either of the three holistic approaches or a combination of any two can be adopted on arboviral disease control. Our findings underline the need for holistic approaches as the best strategy to mitigating and controlling the emerging and reemerging arboviruses.

Lying in wait: the resurgence of dengue virus after the Zika epidemic in Brazil

After the Zika virus (ZIKV) epidemic in the Americas in 2016, both Zika and dengue incidence declined to record lows in many countries in 2017–2018, but in 2019 dengue resurged in Brazil, causing ~2.1 million cases. In this study we use epidemiological, climatological and genomic data to investigate dengue dynamics in recent years in Brazil. First, we estimate dengue virus force of infection (FOI) and model mosquito-borne transmission suitability since the early 2000s. Our estimates reveal that DENV transmission was low in 2017–2018, despite conditions being suitable for viral spread. Our study also shows a marked decline in dengue susceptibility between 2002 and 2019, which could explain the synchronous decline of dengue in the country, partially as a result of protective immunity from prior ZIKV and/or DENV infections. Furthermore, we performed phylogeographic analyses using 69 newly sequenced genomes of dengue virus serotype 1 and 2 from Brazil, and found that the outbreaks in 2018–2019 were caused by local DENV lineages that persisted for 5–10 years, circulating cryptically before and after the Zika epidemic. We hypothesize that DENV lineages may circulate at low transmission levels for many years, until local conditions are suitable for higher transmission, when they cause major outbreaks.

Zika and dengue incidence in the Americas declined in 2017–2018, but dengue resurged in 2019 in Brazil. This study uses epidemiological, climatological and genomic data to show that the decline of dengue may be explained by protective immunity from pre-exposure to ZIKV and/or DENV in prior years.