Data challenges for international health emergencies: lessons learned from ten international COVID-19 driver projects.

The COVID-19 pandemic highlighted the importance of international data sharing and access to improve health outcomes for all. The International COVID-19 Data Alliance (ICODA) programme enabled 12 exemplar or driver projects to use existing health-related data to address major research questions relating to the pandemic, and developed data science approaches that helped each research team to overcome challenges, accelerate the data research cycle, and produce rapid insights and outputs. These approaches also sought to address inequity in data access and use, test approaches to ethical health data use, and make summary datasets and outputs accessible to a wider group of researchers. This Health Policy paper focuses on the challenges and lessons learned from ten of the ICODA driver projects, involving researchers from 19 countries and a range of health-related datasets. The ICODA programme reviewed the time taken for each project to complete stages of the health data research cycle and identified common challenges in areas such as data sharing agreements and data curation. Solutions included provision of standard data sharing templates, additional data curation expertise at an early stage, and a trusted research environment that facilitated data sharing across national boundaries and reduced risk. These approaches enabled the driver projects to rapidly produce research outputs, including publications, shared code, dashboards, and innovative resources, which can all be accessed and used by other research teams to address global health challenges.

Combining Digital and Molecular Approaches Using Health and Alternate Data Sources in a Next-Generation Surveillance System for Anticipating Outbreaks of Pandemic Potential.

Globally, millions of lives are impacted every year by infectious diseases outbreaks. Comprehensive and innovative surveillance strategies aiming at early alert and timely containment of emerging and reemerging pathogens are a pressing priority. Shortcomings and delays in current pathogen surveillance practices further disturbed informing responses, interventions, and mitigation of recent pandemics, including H1N1 influenza and SARS-CoV-2. We present the design principles of the architecture for an early-alert surveillance system that leverages the vast available data landscape, including syndromic data from primary health care, drug sales, and rumors from the lay media and social media to identify areas with an increased number of cases of respiratory disease. In these potentially affected areas, an intensive and fast sample collection and advanced high-throughput genome sequencing analyses would inform on circulating known or novel pathogens by metagenomics-enabled pathogen characterization. Concurrently, the integration of bioclimatic and socioeconomic data, as well as transportation and mobility network data, into a data analytics platform, coupled with advanced mathematical modeling using artificial intelligence or machine learning, will enable more accurate estimation of outbreak spread risk. Such an approach aims to readily identify and characterize regions in the early stages of an outbreak development, as well as model risk and patterns of spread, informing targeted mitigation and control measures. A fully operational system must integrate diverse and robust data streams to translate data into actionable intelligence and actions, ultimately paving the way toward constructing next-generation surveillance systems.

Subsidised housing and diabetes mortality: a retrospective cohort study of 10 million low-income adults in Brazil.

INTRODUCTION: Housing-related factors can be predictors of health, including of diabetes outcomes. We analysed the association between subsidised housing residency and diabetes mortality among a large cohort of low-income adults in Brazil. RESEARCH DESIGN AND METHODS: A cohort of 9 961 271 low-income adults, observed from January 2010 to December 2015, was created from Brazilian administrative records of social programmes and death certificates. We analysed the association between subsidised housing residency and time to diabetes mortality using a Cox model with inverse probability of treatment weighting and regression adjustment. We assessed inequalities in this association by groups of municipality Human Development Index. Diabetes mortality included diabetes both as the underlying or a contributory cause of death. RESULTS: At baseline, the mean age of the cohort was 40.3 years (SD 15.6 years), with a majority of women (58.4%). During 29 238 920 person-years of follow-up, there were 18 775 deaths with diabetes as the underlying or a contributory cause. 340 683 participants (3.4% of the cohort) received subsidised housing. Subsidised housing residents had a higher hazard of diabetes mortality compared with non-residents (HR 1.17; 95% CI 1.05 to 1.31). The magnitude of this association was more pronounced among participants living in municipalities with lower Human Development Index (HR 1.30; 95% CI 1.04 to 1.62). CONCLUSIONS: Subsidised housing residents had a greater risk of diabetes mortality, particularly those living in low socioeconomic status municipalities. This finding suggests the need to intensify diabetes prevention and control actions and prompt treatment of the diabetes complications among subsidised housing residents, particularly among those living in low socioeconomic status municipalities.

Sensory Integration for Postural Control in Rheumatoid Arthritis Revealed by Computerized Dynamic Posturography.

Rheumatoid arthritis (RA) is a systemic autoimmune disease that impairs mobility. How does sensory information influence postural responses in people with RA? The aim of this study was to evaluate the postural control of people with RA during a sensory organization test, comparing how sensory information influences postural responses in people with rheumatoid arthritis compared with healthy people. Participants were 28 women with rheumatoid arthritis (RA group) and 16 women without any rheumatoid disease (Control group CG). The Sensory Organization Test (SOT) was performed on a Smart Balance Master® (NeuroCom International, Inc., Clackamas, OR, USA) and center of pressure (COP) was measured. SOT conditions: SOT1 (eyes open, fixed support surface and surround; SOT2) eyes closed, fixed support surface and surround; and SOT5) eyes closed, sway-referenced support surface, and fixed surround. To compare the demographic and clinical aspects between groups, independent t-test or Mann-Whitney's U-test were used. Differences were found between groups. Between SOT conditions, for CG and RA, COP was faster for SOT-5 than SOT-1, while SOT-1 and SOT-2 presented similar COP velocity. For SOT-2 and SOT-5, COP was larger for the RA group. For both groups, SOT-1 presented the smallest COP, and SOT-5 showed the largest COP.

Profile of COVID-19 in Brazil-risk factors and socioeconomic vulnerability associated with disease outcome: retrospective analysis of population-based registers.

OBJECTIVES: To classify the most up-to-date factors associated with COVID-19 disease outcomes in Brazil. DESIGN: Retrospective study. SETTING: Nationwide Brazilian COVID-19 healthcare registers. PARTICIPANTS: We used healthcare data of individuals diagnosed with mild/moderate (n=70 056 602) or severe (n=2801 380) COVID-19 disease in Brazil between 26 February 2020 and 15 November 2021. MAIN OUTCOME MEASURES: Risk of hospitalisation and mortality affected by demographic, clinical and socioeconomic variables were estimated. The impacts of socioeconomic inequalities on vaccination rates, cases and deaths were also evaluated. RESULTS: 15.6 million SARS-CoV-2 infection cases and 584 761 COVID-19-related deaths occurred in Brazil between 26 February 2020 and 15 November 2021. Overall, men presented a higher odds of death than women (OR=1.14, 95% CI 1.13 to 1.15), but postpartum patients admitted to hospital wards were at increased odds of dying (OR=1.23, 95% CI 1.13 to 1.34) compared with individuals without reported comorbidities. Death in younger age groups was notably higher in most deprived municipalities and also among individuals <40 years belonging to indigenous backgrounds compared with white patients, as shown by descriptive analysis. Ethnic/racial backgrounds exhibited a continuum of decreasing survival chances of mixed-race (OR=1.11, 95% CI 1.10 to 1.12), black (OR=1.34, 95% CI 1.32 to 1.36) and indigenous (OR=1.42, 95% CI 1.31 to 1.54) individuals, while those in most deprived municipalities also presented an increased odds of death (OR=1.38, 95% CI 1.36 to 1.40). Deprivation levels also affect the prompt referral of patients to adequate care. Our results show that the odds of death of individuals hospitalised for less than 4 days is more than double that of patients with close-to-average hospital stays (OR=2.07, 95% CI 2.05 to 2.10). Finally, negative vaccination status also increased the odds of dying from the disease (OR=1.29, 95% CI 1.28 to 1.31). CONCLUSIONS: The data provide evidence that the patterns of COVID-19 mortality in Brazil are influenced by both individual-level health and social risk factors, as well as municipality-level deprivation. In addition, these data suggest that there may be inequalities in the timely provision of appropriate healthcare that are related to municipality-level deprivation.

An IoT-Based System for Monitoring the Health of Guyed Towers in Overhead Power Lines.

The collapse of overhead power line guyed towers is one of the leading causes of power grid failures, subjecting electricity companies to pay considerable, high-value fines. In this way, the current work proposes a novel and complete framework for the remote monitoring of mechanical stresses in guyed towers. The framework method comprises a mesh network for data forwarding and neural networks to improve the performance of Low-Power and Lossy Networks. The method also considers the use of multiple sensors in the sensor fusion technique. As a result, the risk of collapse of guyed cable towers reduces, due to the remote monitoring and preventive actions promoted by the framework. Furthermore, the proposed method uses multiple input variable fusions, such as accelerometers and tension sensors, to estimate the tower's displacement. These estimations help address the structural health of the tower against failures (i.e., loosening of the stay cables, displacement, and vibrations) that can cause catastrophic events, such as tower collapse or even cable rupture.

Estimating underreporting of leprosy in Brazil using a Bayesian approach.

BACKGROUND: Leprosy remains concentrated among the poorest communities in low-and middle-income countries and it is one of the primary infectious causes of disability. Although there have been increasing advances in leprosy surveillance worldwide, leprosy underreporting is still common and can hinder decision-making regarding the distribution of financial and health resources and thereby limit the effectiveness of interventions. In this study, we estimated the proportion of unreported cases of leprosy in Brazilian microregions. METHODOLOGY/PRINCIPAL FINDINGS: Using data collected between 2007 to 2015 from each of the 557 Brazilian microregions, we applied a Bayesian hierarchical model that used the presence of grade 2 leprosy-related physical disabilities as a direct indicator of delayed diagnosis and a proxy for the effectiveness of local leprosy surveillance program. We also analyzed some relevant factors that influence spatial variability in the observed mean incidence rate in the Brazilian microregions, highlighting the importance of socioeconomic factors and how they affect the levels of underreporting. We corrected leprosy incidence rates for each Brazilian microregion and estimated that, on average, 33,252 (9.6%) new leprosy cases went unreported in the country between 2007 to 2015, with this proportion varying from 8.4% to 14.1% across the Brazilian States. CONCLUSIONS/SIGNIFICANCE: The magnitude and distribution of leprosy underreporting were adequately explained by a model using Grade 2 disability as a marker for the ability of the system to detect new missing cases. The percentage of missed cases was significant, and efforts are warranted to improve leprosy case detection. Our estimates in Brazilian microregions can be used to guide effective interventions, efficient resource allocation, and target actions to mitigate transmission.

A control framework to optimize public health policies in the course of the COVID-19 pandemic.

The SARS-CoV-2 pandemic triggered substantial economic and social disruptions. Mitigation policies varied across countries based on resources, political conditions, and human behavior. In the absence of widespread vaccination able to induce herd immunity, strategies to coexist with the virus while minimizing risks of surges are paramount, which should work in parallel with reopening societies. To support these strategies, we present a predictive control system coupled with a nonlinear model able to optimize the level of policies to stop epidemic growth. We applied this system to study the unfolding of COVID-19 in Bahia, Brazil, also assessing the effects of varying population compliance. We show the importance of finely tuning the levels of enforced measures to achieve SARS-CoV-2 containment, with periodic interventions emerging as an optimal control strategy in the long-term.

Assessing the nationwide impact of COVID-19 mitigation policies on the transmission rate of SARS-CoV-2 in Brazil.

COVID-19 is now identified in almost all countries in the world, with poorer regions being particularly more disadvantaged to efficiently mitigate the impacts of the pandemic. In the absence of efficient therapeutics or large-scale vaccination, control strategies are currently based on non-pharmaceutical interventions, comprising changes in population behavior and governmental interventions, among which the prohibition of mass gatherings, closure of non-essential establishments, quarantine and movement restrictions. In this work we analyzed the effects of 707 governmental interventions published up to May 22, 2020, and population adherence thereof, on the dynamics of COVID-19 cases across all 27 Brazilian states, with emphasis on state capitals and remaining inland cities. A generalized SEIR (Susceptible, Exposed, Infected and Removed) model with a time-varying transmission rate (TR), that considers transmission by asymptomatic individuals, is presented. We analyze the effect of both the extent of enforced measures across Brazilian states and population movement on the changes in the TR and effective reproduction number. The social mobility reduction index, a measure of population movement, together with the stringency index, adapted to incorporate the degree of restrictions imposed by governmental regulations, were used in conjunction to quantify and compare the effects of varying degrees of policy strictness across Brazilian states. Our results show that population adherence to social distance recommendations plays an important role for the effectiveness of interventions and represents a major challenge to the control of COVID-19 in low- and middle-income countries.

[Covid-19 in the Northeast of Brazil: from lockdown to the relaxation of social distancing measures]. / Covid-19 no nordeste do Brasil: entre o lockdown e o relaxamento das medidas de distanciamento social.

Even in the period when the Covid-19 pandemic was on the rise in the Northeast of Brazil, the relaxation of social distancing measures was introduced. The scope of the study is to assess, in the light of the epidemiological-sanitary situation in the region, the suitability of relaxation of social distancing measures. Based on the WHO guidelines for relaxation of social distancing, operational indicators were created and analyzed for each guideline in the context of the Northeast. To analyze the behavior of the epidemic, according to selected indicators, Joinpoint trend analysis techniques, heat maps, rate ratios and time trends between capitals and the state interior were compared. The weekly growth peak of the epidemic occurred in May-July 2020 (epidemiological weeks 19 to 31). In most capitals, there was no simultaneous downward trend in the number of cases and deaths in the 14 days prior to flexibilization. In all states the number of tests performed was insufficient. In epidemiological week 24, the state percentages of ICU/Covid-19 bed occupancy were close to or above 70%. The epidemiological situation of the nine Northeastern state capitals analyzed here did not meet criteria and parameters recommended by the World Health Organization for the relaxation of social distancing measures.

Mesmo no período em que a pandemia de Covid-19 encontrava-se em crescimento no Nordeste do Brasil, iniciou-se a adoção de medidas de flexibilização do distanciamento social. O objetivo do estudo é o de avaliar a pertinência das propostas de flexibilização, tomando-se em conta a situação da pandemia em cada local e o momento em que foram adotadas. Tendo como referência as diretrizes da OMS, foram construídos e analisados indicadores operacionais para cada diretriz, no contexto da região Nordeste. Para análise do comportamento da epidemia, conforme indicadores selecionados, foram usadas técnicas de Joinpoint Trend Analysis, mapas de calor, razão de taxas e comparação da tendência temporal entre capitais e interior dos estados. O pico do crescimento semanal ocorreu em maio-julho/2020 (semanas epidemiológicas 19 a 31). Na maioria das capitais não se observou tendência decrescente simultânea do número de casos e óbitos nos 14 dias prévios à flexibilização. Em todos os estados o quantitativo de testes realizados foi insuficiente. Na semana epidemiológica 24 os percentuais estaduais de ocupação de leitos de UTI/Covid-19 foram próximos ou superiores 70%. A situação epidemiológica das nove capitais dos estados do Nordeste, no momento em que a decisão de flexibilização foi tomada, mostra que nenhuma delas atendia aos critérios e parâmetros recomendados pela OMS.

Classification algorithm for congenital Zika Syndrome: characterizations, diagnosis and validation.

Zika virus was responsible for the microcephaly epidemic in Brazil which began in October 2015 and brought great challenges to the scientific community and health professionals in terms of diagnosis and classification. Due to the difficulties in correctly identifying Zika cases, it is necessary to develop an automatic procedure to classify the probability of a CZS case from the clinical data. This work presents a machine learning algorithm capable of achieving this from structured and unstructured available data. The proposed algorithm reached 83% accuracy with textual information in medical records and image reports and 76% accuracy in classifying data without textual information. Therefore, the proposed algorithm has the potential to classify CZS cases in order to clarify the real effects of this epidemic, as well as to contribute to health surveillance in monitoring possible future epidemics.

Mathematical modeling of COVID-19 in 14.8 million individuals in Bahia, Brazil.

COVID-19 is affecting healthcare resources worldwide, with lower and middle-income countries being particularly disadvantaged to mitigate the challenges imposed by the disease, including the availability of a sufficient number of infirmary/ICU hospital beds, ventilators, and medical supplies. Here, we use mathematical modelling to study the dynamics of COVID-19 in Bahia, a state in northeastern Brazil, considering the influences of asymptomatic/non-detected cases, hospitalizations, and mortality. The impacts of policies on the transmission rate were also examined. Our results underscore the difficulties in maintaining a fully operational health infrastructure amidst the pandemic. Lowering the transmission rate is paramount to this objective, but current local efforts, leading to a 36% decrease, remain insufficient to prevent systemic collapse at peak demand, which could be accomplished using periodic interventions. Non-detected cases contribute to a ∽55% increase in R0. Finally, we discuss our results in light of epidemiological data that became available after the initial analyses.

Scaling effect in COVID-19 spreading: The role of heterogeneity in a hybrid ODE-network model with restrictions on the inter-cities flow.

The new Covid-19 pandemic has left traces of suffering and devastation to individuals of almost all countries worldwide and severe impact on the global economy. Understanding the clinical characteristics, interactions with the environment, and the variables that favor or hinder its dissemination help the public authorities in the fight and prevention, leading for a rapid response in society. Using models to estimate contamination scenarios in real time plays an important role. Population compartments models based on ordinary differential equations (ODE) for a given region assume two homogeneous premises, the contact mechanisms and diffusion rates, disregarding heterogeneous factors as different contact rates for each municipality and the flow of contaminated people among them. This work considers a hybrid model for covid-19, based on local SIR models and the population flow network among municipalities, responsible for a complex lag dynamic in their contagion curves. Based on actual infection data, local contact rates ( ß ) are evaluated. The epidemic evolution at each municipality depends on the local SIR parameters and on the inter-municipality transport flow. When heterogeneity of ß values and flow network are included, forecasts differ from those of the homogeneous ODE model. This effect is more relevant when more municipalities are considered, hinting that the latter overestimates new cases. In addition, mitigation scenarios are assessed to evaluate the effect of earlier interventions reducing the inter-municipality flux. Restricting the flow between municipalities in the initial stage of the epidemic is fundamental for flattening the contamination curve, highlighting advantages of a contamination lag between the capital curve and those of other municipalities in the territories.

New Insights on the Zika Virus Arrival in the Americas and Spatiotemporal Reconstruction of the Epidemic Dynamics in Brazil.

Zika virus (ZIKV) became a worldwide public health emergency after its introduction in the Americas. Brazil was implicated as central in the ZIKV dispersion, however, a better understanding of the pathways the virus took to arrive in Brazil and the dispersion within the country is needed. An updated genome dataset was assembled with publicly available data. Bayesian phylogeography methods were applied to reconstruct the spatiotemporal history of ZIKV in the Americas and with more detail inside Brazil. Our analyses reconstructed the Brazilian state of Pernambuco as the likely point of introduction of ZIKV in Brazil, possibly during the 2013 Confederations Cup. Pernambuco played an important role in spreading the virus to other Brazilian states. Our results also underscore the long cryptic circulation of ZIKV in all analyzed locations in Brazil. Conclusions: This study brings new insights about the early moments of ZIKV in the Americas, especially regarding the Brazil-Haiti cluster at the base of the American clade and describing for the first time migration patterns within Brazil.

Case Fatality Rate Related to Microcephaly Congenital Zika Syndrome and Associated Factors: A Nationwide Retrospective Study in Brazil †.

BACKGROUND: The clinical manifestations of microcephaly/congenital Zika syndrome (microcephaly/CZS) have harmful consequences on the child's health, increasing vulnerability to childhood morbidity and mortality. This study analyzes the case fatality rate and child-maternal characteristics of cases and deaths related to microcephaly/CZS in Brazil, 2015-2017. METHODS: Population-based study developed by linkage of three information systems. We estimate frequencies of cases, deaths, case fatality rate related to microcephaly/CZS according to child and maternal characteristics and causes of death. Multivariate logistic regression models were applied. RESULTS: The microcephaly/CZS case fatality rate was 10% (95% CI 9.2-10.7). Death related to microcephaly/CZS was associated to moderate (OR = 2.15; 95% CI 1.63-2.83), and very low birth weight (OR = 3.77; 95% CI 2.20-6.46); late preterm births (OR = 1.65; 95% CI 1.21-2.23), Apgar < 7 at 1st (OR = 5.98; 95% CI 4.46-8.02) and 5th minutes (OR = 4.13; 95% CI 2.78-6.13), among others. CONCLUSIONS: A high microcephaly/CZS case fatality rate and important factors associated with deaths related to this syndrome were observed. These results can alert health teams to these problems and increase awareness about the factors that may be associated with worse outcomes.

COVID-19 in Northeast Brazil: achievements and limitations in the responses of the state governments. / COVID-19 no Nordeste brasileiro: sucessos e limitações nas respostas dos governos dos estados.

The COVID-19 pandemic has been most severe in the poorest regions of Brazil, such as the states of the Northeast Region. The lack of national policies for pandemic control forced state and municipal authorities to implement public health measures. The aim of this study is to show the effect of these measures on the epidemic. The highest incidence of COVID-19 among the nine states in the Northeast was recorded in Sergipe, Paraíba and Ceará. Piauí, Paraíba and Ceará were the states that most tested. Factors associated with transmission included the high proportion of people in informal work. States with international airports played an important role in the entry of the virus and the initial spread, especially Ceará. All states applied social distancing measures, banned public events and closed schools. The response was a significant increase in social distancing, especially in Ceará and Pernambuco, a decline in the reproduction rate (Rt), and a separation of the curve of observed cases versus expected cases if the non-pharmacological interventions had not been implemented in all states. Poverty, inequality, and the high rates of informal work provide clues to the intensity of COVID-19 in the region. On the other hand, the measures taken early by the governments mitigated the effects of the pandemic.

No Brasil, a pandemia da COVID-19 tem sido severa nos estados das regiões mais pobres, como o Nordeste. A falta de políticas nacionais para controle da pandemia levou as autoridades estaduais e municipais a implementarem medidas de saúde pública. O objetivo deste estudo é mostrar o efeito dessas medidas na epidemia. A maior incidência da COVID-19 entre os nove estados do Nordeste foi registrada em Sergipe, Paraíba e Ceará. O Piauí, a Paraíba e Ceará foram os que mais testaram. Muitos estados apresentavam alta proporção de pessoas em trabalho informal. Estados com aeroportos internacionais tiveram importante papel na entrada e disseminação inicial do vírus, em especial o Ceará. Todos os estados aplicaram medidas de distanciamento social, proibição de eventos públicos e fechamento de unidades de ensino. As respostas foram o aumento significativo de distanciamento social, em especial Ceará e Pernambuco, a queda do número de reprodução (Rt) e a separação da curva dos casos observados da curva dos casos esperados sem as intervenções não medicamentosas em todos os estados. A pobreza, a desigualdade e as altas taxas de trabalho informal fornecem pistas do porquê da intensidade da COVID-19 na região. Por outro lado, as medidas de mitigação tomadas precocemente pelos governantes amenizaram os efeitos da pandemia.

Analise dos dados biométricos gerados por interferometria comparada com Scheimpflug / Analysis of biometric data generated by interferometry compared with Scheimpflug

Resumo Objetivo: Observar o grau de concordância das variáveis analisadas entre os dispositivos IOL Master 500 e Pentacam AXL e descrever as medias Métodos: Foram analisados 35 prontuários, totalizando 61 olhos. Todos os pacientes se submeteram à avaliação biométrica nos dois dispositivos, no período de agosto de 2018 a agosto de 2019. Os dados coletados foram: idade, sexo, profundidade da câmara anterior, comprimento axial, K1, K2, poder dióptrico da LIO e alvo refracional. Resultados: As médias das variáveis analisadas entre os dispositivos de biométricos óptica em questão tiveram diferença estatisticamente significante (p<0,05). A regressão linear não apontou influência de nenhuma das variáveis da câmara anterior na diferença de valores do poder dióptrico da LIO e do alvo refracional entre os dispositivos. Conclusão: Não houve concordância estatística entre os dispositivos para as variáveis analisadas. Portanto, deve se evitar intercambiar o uso do Pentacam AXL com o IOL Master 500.

Abstract Objective: Observe the agreement between IOL Master 500 and Pentacam AXL and describe the averages. Methods: We analyzed 35 medical records, totaling 61 eyes. All patients underwent biometric evaluation on both devices from August 2018 to August 2019. The data collected were: age, gender, anterior chamber depth, axial length, K1, K2, biometrics and IOL target. Results: The averages of the variables analyzed between the optical biometric devices in question had a statistically significant difference (p <0.05). Linear regression showed no influence of any anterior chamber variables on the difference in biometrics and target values between the devices. Conclusion: There was no statistical agreement between the devices for the analyzed variables. Therefore, the interchange of Pentacam AXL with IOL Master 500 should be avoided.

The global scientific research response to the public health emergency of Zika virus infection.

BACKGROUND: Science studies have been a field of research for different knowledge areas, and they have been successfully used to analyse the construction of scientific knowledge, practice and dissemination. In this study, we aimed to verify how the Zika epidemic has moulded the scientific articles published worldwide by analysing international collaborations and the knowledge landscape through time, as well as research topics and country involvement. METHODOLOGY: We searched the Web of Science (WoS), Scopus and PubMed for studies published up to 31st December 2018 on Zika using the search terms "zika", "zkv" or "zikv". We analysed the scientific production regarding which countries have published the most, on which topics, as well as country level collaboration. We performed a scientometric analysis of research on Zika focusing on knowledge mapping and the scientific research path over time and space. FINDINGS: We found two well defined research areas divided into three subtopics accounting for six clusters. With regard to country analysis, the USA and Brazil were the countries with the highest numbers of publications on Zika. China entered as a new player focusing on specific research areas. When we took into consideration the epidemics and reported cases, Brazil and France were the leading research countries on related topics. As for international collaboration, the USA followed by England and France stand out as the main hubs. The research areas most published included public health-related topics from 2015 until the very beginning of 2016, followed by an increase in topics related to the clinical aspects of the disease in 2016 and the emergence of laboratory research in 2017/2018. CONCLUSIONS: Mapping the response to Zika, a public health emergency, demonstrated a clear pattern of the participation of countries in the scientific advances. The pattern of knowledge production found in this study represented varying country perspectives, research capacity and interests based first on their level of exposure to the epidemic and second on their financial positions regarding science.

Interdependence between confirmed and discarded cases of dengue, chikungunya and Zika viruses in Brazil: A multivariate time-series analysis.

The co-circulation of different arboviruses in the same time and space poses a significant threat to public health given their rapid geographic dispersion and serious health, social, and economic impact. Therefore, it is crucial to have high quality of case registration to estimate the real impact of each arboviruses in the population. In this work, a Vector Autoregressive (VAR) model was developed to investigate the interrelationships between discarded and confirmed cases of dengue, chikungunya, and Zika in Brazil. We used data from the Brazilian National Notifiable Diseases Information System (SINAN) from 2010 to 2017. There were three peaks in the series of dengue notification in this period occurring in 2013, 2015 and in 2016. The series of reported cases of both Zika and chikungunya reached their peak in late 2015 and early 2016. The VAR model shows that the Zika series have a significant impact on the dengue series and vice versa, suggesting that several discarded and confirmed cases of dengue could actually have been cases of Zika. The model also suggests that the series of confirmed and discarded chikungunya cases are almost independent of the cases of Zika, however, affecting the series of dengue. In conclusion, co-circulation of arboviruses with similar symptoms could have lead to misdiagnosed diseases in the surveillance system. We argue that the routinely use of mathematical and statistical models in association with traditional symptom-surveillance could help to decrease such errors and to provide early indication of possible future outbreaks. These findings address the challenges regarding notification biases and shed new light on how to handle reported cases based only in clinical-epidemiological criteria when multiples arboviruses co-circulate in the same population.