ORCID Profile
0000-0002-7941-0285
Current Organisation
Universidade Nova de Lisboa
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Publisher: Cold Spring Harbor Laboratory
Date: 18-10-2019
DOI: 10.1101/19009555
Abstract: The recent emergence and established presence of Aedes aegypti in the Autonomous Region of Madeira, Portugal, was responsible for the first autochthonous outbreak of dengue in Europe. The island has not reported any dengue cases since the outbreak in 2012. However, there is a high risk that an introduction of the virus would result in another autochthonous outbreak given the presence of the vector and permissive environmental conditions. Understanding the dynamics of a potential epidemic is critical for targeted local control strategies. Here, we adopt a deterministic model for the transmission of dengue in Aedes aegypti mosquitoes. The model integrates empirical and mechanistic parameters for virus transmission, under seasonally varying temperatures for Funchal, Madeira Island. We examine the epidemic dynamics as triggered by the arrival date of an infectious in idual the influence of seasonal temperature mean and variation on the epidemic dynamics and performed a sensitivity analysis on the following quantities of interest: the epidemic peak size, time to peak and the final epidemic size. Our results demonstrate the potential for summer to early winter transmission of dengue, with the arrival date significantly affecting the distribution of the timing and peak size of the epidemic. Mid-summer to early autumn arrivals are more likely to produce larger epidemics within a short peak time. Epidemics within this favorable period had an average of 18% of the susceptible population infected at the peak, at an average peak time of 70 days. We also demonstrated that seasonal temperature variation dramatically affects the epidemic dynamics, with warmer starting temperatures producing peaks more quickly after an introduction and larger epidemics. Overall, our quantities of interest were most sensitive to variance in the date of arrival, seasonal temperature, biting rate, transmission rates, and the mosquito population the magnitude of sensitivity differs across quantities. Our model could serve as a useful guide in the development of effective local control and mitigation strategies for dengue fever in Madeira Island. The presence of Aedes aegypti mosquitoes in Madeira Island had recently caused the first local outbreak of dengue in Europe. The island is at risk of another local transmission if triggered by the introduction of the dengue virus by an infected person. Using a mathematical model for the transmission of dengue, we examine the dynamics of a potential epidemic triggered by the arrival of an infected person on the island. We also examine the impact of seasonal temperature variation on the epidemic dynamics. Our results show the potential for summer to early winter transmission of dengue on the island, and that the arrival date of an infectious person affects the distribution of the timing and peak size of the epidemic. Arrival dates during mid-summer to early autumn were more likely to produce larger epidemic peak size within a short time. We also show that seasonal temperature variation dramatically affects the epidemic dynamics. With warmer starting temperatures, epidemics peak more rapidly and produce a larger epidemic size. Our model could be useful to estimate the risk of an epidemic outbreak and as a guide for local control and mitigation strategies for dengue on the island.
Publisher: Ubiquity Press, Ltd.
Date: 2019
DOI: 10.5334/AOGH.2307
Publisher: MDPI AG
Date: 27-06-2022
Abstract: Maternity health care services utilization determines maternal and neonate outcomes. Evidence about factors associated with composite non-utilization of four or more antenatal consultations and intrapartum health care services is needed in Mozambique. This study uses data from the 2015 nationwide Mozambique’s Malaria, Immunization and HIV Indicators Survey. At selected representative households, women (n = 2629) with child aged up to 3 years answered a standardized structured questionnaire. Adjusted binary logistic regression assessed associations between women-child pairs characteristics and non-utilization of maternity health care. Seventy five percent (95% confidence interval (CI) = 71.8–77.7%) of women missed a health care cascade step during their last pregnancy. Higher education (adjusted odds ratio (AOR) = 0.65 95% CI = 0.46–0.91), lowest wealth (AOR = 2.1 95% CI = 1.2–3.7), rural residency (AOR = 1.5 95% CI = 1.1–2.2), living distant from health facility (AOR = 1.5 95% CI = 1.1–1.9) and unknown HIV status (AOR = 1.9 95% CI = 1.4–2.7) were factors associated with non-utilization of the maternity health care cascade. The study highlights that, by 2015, recommended maternity health care cascade utilization did not cover 7 out of 10 pregnant women in Mozambique. Unfavorable sociodemographic and economic factors increase the relative odds for women not being covered by the maternity health care cascade.
Publisher: Springer Science and Business Media LLC
Date: 16-06-2020
DOI: 10.1038/S41598-020-66650-1
Abstract: The geographical spread of dengue is a global public health concern. This is largely mediated by the importation of dengue from endemic to non-endemic areas via the increasing connectivity of the global air transport network. The dynamic nature and intrinsic heterogeneity of the air transport network make it challenging to predict dengue importation. Here, we explore the capabilities of state-of-the-art machine learning algorithms to predict dengue importation. We trained four machine learning classifiers algorithms, using a 6-year historical dengue importation data for 21 countries in Europe and connectivity indices mediating importation and air transport network centrality measures. Predictive performance for the classifiers was evaluated using the area under the receiving operating characteristic curve, sensitivity, and specificity measures. Finally, we applied practical model-agnostic methods, to provide an in-depth explanation of our optimal model’s predictions on a global and local scale. Our best performing model achieved high predictive accuracy, with an area under the receiver operating characteristic score of 0.94 and a maximized sensitivity score of 0.88. The predictor variables identified as most important were the source country’s dengue incidence rate, population size, and volume of air passengers. Network centrality measures, describing the positioning of European countries within the air travel network, were also influential to the predictions. We demonstrated the high predictive performance of a machine learning model in predicting dengue importation and the utility of the model-agnostic methods to offer a comprehensive understanding of the reasons behind the predictions. Similar approaches can be utilized in the development of an operational early warning surveillance system for dengue importation.
Publisher: Cold Spring Harbor Laboratory
Date: 18-10-2019
DOI: 10.1101/19009589
Abstract: The spread of dengue through global human mobility is a major public health concern. A key challenge is understanding the transmission pathways and mediating factors that characterized the patterns of dengue importation into non-endemic areas. Utilizing a network connectivity-based approach, we analyze the importation patterns of dengue fever into European countries. Seven connectivity indices were developed to characterize the role of the air passenger traffic, seasonality, incidence rate, geographical proximity, epidemic vulnerability, and wealth of a source country, in facilitating the transport and importation of dengue fever. We used generalized linear mixed models (GLMMs) to examine the relationship between dengue importation and the connectivity indices while accounting for the air transport network structure. We also incorporated network autocorrelation within a GLMM framework to investigate the propensity of a European country to receive an imported case, by virtue of its position within the air transport network. The connectivity indices and dynamical processes of the air transport network were strong predictors of dengue importation in Europe. With more than 70% of the variation in dengue importation patterns explained. We found that transportation potential was higher for source countries with seasonal dengue activity, high passenger traffic, high incidence rates, lower economic status, and geographical proximity to a destination country in Europe. We also found that position of a European country within the air transport network was a strong predictor of the country’s propensity to receive an imported case. Our findings provide evidence that the importation patterns of dengue into Europe can be largely explained by appropriately characterizing the heterogeneities of the source, and topology of the air transport network. This contributes to the foundational framework for building integrated predictive models for bio-surveillance of dengue importation.
Publisher: MDPI AG
Date: 31-05-2022
DOI: 10.3390/HEALTHCARE10061013
Abstract: Information about factors underlying peripartum complications is needed to inform health programs in Mozambique. This retrospective study covered the period from January 2013 to December 2018 and was performed at three rural-district hospitals in southern Mozambique, aiming at assessing factors associated with caesarean and peripartum complications. Data were extracted by clinical criteria-based audits on randomly select clients’ files. Logistical regression was used to identify factors associated with peripartum complications. Amongst 5068 audited files, women mean age was 25 years (Standard Deviation (SD) = 7), gestational age was 38 weeks (SD = 2), 25% had “high obstetric-risk” and 19% delivered by caesarean. Factors significantly associated with caesarean included being transferred [Adjusted Odds Ratio (aOR) =1.8 95% Confidence Interval (95%CI) = 1.3–2.6], preecl sia [aOR (95%CI) = 2.0 (1.2–3.3)], age [aOR (95%CI) = 0.96 (0.93–0.99)] and “high obstetric-risk” [aOR (95%CI) = 0.54 (0.37–0.78)]. Factors significantly associated with neonatal complication included mother being transferred [aOR (95%CI) = 2.1 (1.8–2.6)], “high obstetric-risk” [aOR (95%CI) = 1.6 (1.3–1.96)], preecl sia [aOR (95%CI) = 1.5 (1.2–1.8), mother’s age [aOR (95%CI) = −2% (−3%, −0.1%)] and gestational age [aOR (95%CI) = −8% (−13%, −6%)] increment. This study identified amendable factors associated with peripartum complications in rural referral health settings. Strengthening hospitals’ performance assurance is critical to address the identified factors and improve peripartum outcomes for mothers-neonate dyads.
Publisher: Public Library of Science (PLoS)
Date: 12-03-2020
Publisher: Public Library of Science (PLoS)
Date: 05-10-2020
Publisher: Cold Spring Harbor Laboratory
Date: 29-11-2019
DOI: 10.1101/19013383
Abstract: The geographical spread of dengue is a global public health concern. This is largely mediated by the importation of dengue from endemic to non-endemic areas via the increasing connectivity of the global air transport network. The dynamic nature and intrinsic heterogeneity of the air transport network make it challenging to predict dengue importation. Here, we explore the capabilities of state-of-the-art machine learning algorithms to predict dengue importation. We trained four machine learning classifiers algorithms, using a 6-year historical dengue importation data for 21 countries in Europe and connectivity indices mediating importation and air transport network centrality measures. Predictive performance for the classifiers was evaluated using the area under the receiving operating characteristic curve, sensitivity, and specificity measures. Finally, we applied practical model-agnostic methods, to provide an in-depth explanation of our optimal model’s predictions on a global and local scale. Our best performing model achieved high predictive accuracy, with an area under the receiver operating characteristic score of 0.94 and a maximized sensitivity score of 0.88. The predictor variables identified as most important were the source country’s dengue incidence rate, population size, and volume of air passengers. Network centrality measures, describing the positioning of European countries within the air travel network, were also influential to the predictions. We demonstrated the high predictive performance of a machine learning model in predicting dengue importation and the utility of the model-agnostic methods to offer a comprehensive understanding of the reasons behind the predictions. Similar approaches can be utilized in the development of an operational early warning surveillance system for dengue importation.
No related grants have been discovered for Maria do Rosário Oliveira Martins.