A Review of Artificial Intelligence and Machine Learning in Product Life Cycle Management.

The pursuit of harnessing data for knowledge creation has been an enduring quest, with the advent of machine learning and artificial intelligence (AI) marking significant milestones in this journey. Machine Learning (ML), a subset of AI, emerged as the practice of employing mathematical models to enable computers to learn and improve autonomously based on their experiences. In the pharmaceutical and biopharmaceutical sectors, a significant portion of manufacturing data remains untapped or insufficient for practical use. Recognizing the potential advantages of leveraging available data for process design and optimization, manufacturers face the daunting challenge of data utilization. Diverse proprietary data formats and parallel data generation systems compound the complexity. The transition to Pharma 4.0 necessitates a paradigm shift in data capture for manufacturing and process operations. This paper highlights the pivotal role of artificial intelligence in converting process data into actionable knowledge to support critical functions throughout the whole process life cycle. Furthermore, it underscores the importance of maintaining compliance with data integrity guidelines, as mandated by regulatory bodies globally. Embracing AI-driven transformations is a crucial step toward shaping the future of the pharmaceutical industry, ensuring its competitiveness and resilience in an evolving landscape.

Holistic in silico developability assessment of novel classes of small proteins using publicly available sequence-based predictors.

The development of novel therapeutic proteins is a lengthy and costly process, with an average attrition rate of 91% (Thomas et al. Clinical Development Success Rates and Contributing Factors 2011-2020, 2021). To increase the probability of success and ensure robust drug supply beyond approval, it is essential to assess the developability profile of new potential drug candidates as early and broadly as possible in development (Jain et al. MAbs, 2023. https://doi.org/10.1016/j.copbio.2011.06.002 ). Predicting these properties in silico is expected to be the next leap in innovation as it would enable significantly reduced development timelines combined with broader screens at lower costs. However, developing predictive algorithms typically requires substantial datasets generated under very defined conditions, a limiting factor especially for new classes of therapeutic proteins that hold immense clinical promise. Here we describe a strategy for assessing the developability of a novel class of small therapeutic Anticalin® proteins using machine learning in conjunction with a knowledge-driven approach. The knowledge-driven approach considers developability attributes such as aggregation propensity, charge variants, immunogenicity, specificity, thermal stability, hydrophobicity, and potential post-translational modifications, to calculate a holistic developability score. Based on sequence-derived descriptors as input parameters we established novel statistical models designed to predict the developability scores for Anticalin proteins. The best models yielded low root mean square errors across the entire dataset and were further validated by removing input data from individual screening campaigns and predicting developability scores for those drug candidates. The adoption of the described workflow will enable significantly streamlined preclinical development of Anticalin drug candidates and could potentially be applied to other therapeutic protein scaffolds.

SARS-CoV-2 transmission in a highly vulnerable population of Brazil: a household cohort study.

Background: Household transmission studies seek to understand the transmission dynamics of a pathogen by estimating the risk of infection from household contacts and community exposures. We estimated within/extra-household SARS-CoV-2 infection risk and associated factors in a household cohort study in one of the most vulnerable neighbourhoods in Rio de Janeiro city. Methods: Individuals ≥1 years-old with suspected or confirmed COVID-19 in the past 30 days (index cases) and household members aged ≥1 year were enrolled and followed at 14 and 28 days (study period November/2020-December/2021). RT-PCR testing, COVID-19 symptoms, and SARS-CoV-2 serologies were ascertained in all visits. Chain binomial household transmission models were fitted using data from 2024 individuals (593 households). Findings: Extra-household infection risk was 74.2% (95% credible interval [CrI] 70.3-77.8), while within-household infection risk was 11.4% (95% CrI 5.7-17.2). Participants reporting having received two doses of a COVID-19 vaccine had lower extra-household (68.9%, 95% CrI 57.3-77.6) and within-household (4.1%, 95% CrI 0.4-16.6) infection risk. Within-household infection risk was higher among participants aged 10-19 years, from overcrowded households, and with low family income. Contrastingly, extra-household infection risk was higher among participants aged 20-29 years, unemployed, and public transportation users. Interpretation: Our study provides important insights into COVID-19 household/community transmission in a vulnerable population that resided in overcrowded households and who struggled to adhere to lockdown policies and social distancing measures. The high extra-household infection risk highlights the extreme social vulnerability of this population. Prioritising vaccination of the most socially vulnerable could protect these individuals and reduce widespread community transmission. Funding: Fundação Oswaldo Cruz, CNPq, FAPERJ, Royal Society, Instituto Serrapilheira, FAPESP.

Disentangling the seasonality effects of malaria transmission in the Brazilian Amazon basin.

The evidence of seasonal patterns in malaria epidemiology in the Brazilian Amazon basin indicates the need for a thorough investigation of seasonality in this last and heterogeneous region. Additionally, since these patterns are linked to climate variables, malaria models should also incorporate them. This study applies wavelet analysis to incidence data from 2003 to 2020 in the Epidemiological Surveillance System for Malaria (SIVEP-Malaria) database. A mathematical model with climate-dependent parametrization is proposed to study counts of malaria cases over time based on notification data, temperature and rainfall. The wavelet analysis reveals marked seasonality in states Amazonas and Amapá throughout the study period, and from 2003 to 2012 in Pará. However, these patterns are not as marked in other states such as Acre and Pará in more recent years. The wavelet coherency analysis indicates a strong association between incidence and temperature, especially for the municipalities of Macapá and Manaus, and a similar association for rainfall. The mathematical model fits well with the observed temporal trends in both municipalities. Studies on climate-dependent mathematical models provide a good assessment of the baseline epidemiology of malaria. Additionally, the understanding of seasonality effects and the application of models have great potential as tools for studying interventions for malaria control.

Nanofluidic resistive pulse sensing for characterization of extracellular vesicles.

This paper describes the development, design and characterization of a resistive pulse sensing (RPS) system for the analysis of size distributions of extracellular vesicles (EVs). The system is based on microfluidic chips fabricated using soft-lithography and operated in pressure-driven mode. This fabrication approach provided reproducible pore dimensions and the best performing chip design enabled, without calibration, sizing of both 252 nm and 460 nm test particles within 8% of theoretically calculated values, based on the size specifications provided by suppliers. The number concentration measurement had higher variations and without calibration provided estimates within an order of magnitude, for sample concentrations across 4 orders of magnitude. The RPS chips could also measure successfully EVs and other biological nanoparticles in purified samples from cell culture media and human serum. A compact, fast and inexpensive RPS system based on this design could be an attractive alternative to current gold-standard techniques for routine characterization of EV samples.

Analysis of Uncertainty and Sensitivity in Tailings Dam Breach-Runout Numerical Modelling.

Tailings dam breaches (TDBs) and subsequent flows can pose significant risk to public safety, the environment, and the economy. Numerical runout models are used to simulate potential tailings flows and understand their downstream impacts. Due to the complex nature of the breach-runout processes, the mobility and downstream impacts of these types of failures are highly uncertain. We applied the first-order second-moment (FOSM) methodology to a database of 11 back-analyzed historical tailings flows to evaluate uncertainties in TDB runout modelling and conducted a sensitivity analysis to identify key factors contributing to the variability of the HEC-RAS model output, including at different locations along the runout path. The results indicate that prioritizing resources toward advancements in estimating the values of primary contributors to the sensitivity of the selected model outputs is necessary for more reliable model results. We found that the total released volume is among the top contributors to the sensitivity of modelled inundation area and maximum flow depth, while surface roughness is among the top contributors to the sensitivity of modelled maximum flow velocity and flow front arrival time. However, the primary contributors to the sensitivity of the model outputs varied depending on the case study; therefore, the selection of appropriate rheological models and consideration of site-specific conditions are crucial for accurate predictions. The study proposes and demonstrates the FOSM methodology as an approximate probabilistic approach to model-based tailings flow runout prediction, which can help improve the accuracy of risk assessments and emergency response plans. Supplementary Information: The online version contains supplementary material available at 10.1007/s10230-024-00970-w.

Las roturas de presas de relaves (TDBs) y los flujos subsiguientes pueden suponer un riesgo significativo para la seguridad pública, el medio ambiente y la economía. Los modelos numéricos de desbordamiento se utilizan para simular posibles flujos de relaves y comprender su impacto aguas abajo. Debido a la naturaleza compleja de los procesos de rotura-desbordamiento, la movilidad y los impactos aguas abajo de este tipo de fallos tienen mucha incertidumbre. Se aplicó la metodología del segundo-momento de primer-orden (FOSM) a una base de datos de 11 flujos históricos de relaves analizados retrospectivamente para evaluar las incertidumbres en la modelización del desbordamiento de TDB y se realizó un análisis de sensibilidad para identificar los factores clave que contribuyen a la variabilidad de los resultados del modelo HEC-RAS, incluso en diferentes ubicaciones a lo largo de la trayectoria de fuga. Los resultados indican que es necesario priorizar los recursos hacia avances en la estimación de los valores de los principales contribuyentes a la sensibilidad de los resultados del modelo seleccionado para obtener resultados más fiables del modelo. El volumen total liberado se encuentra entre los principales contribuyentes a la sensibilidad del área de inundación modelizada y la profundidad máxima del flujo, mientras que la rugosidad de la superficie se encuentra entre los principales contribuyentes a la sensibilidad de la velocidad máxima del flujo modelizado y el tiempo de llegada del frente de flujo. Sin embargo, los principales factores que contribuyen a la sensibilidad de los resultados del modelo varían dependiendo del caso de estudio; por lo tanto, la selección de modelos reológicos apropiados y la consideración de las condiciones específicas del emplazamiento son cruciales para obtener predicciones precisas. El estudio propone y muestra la metodología FOSM como un enfoque probabilístico aproximado para la predicción de la extensión de flujos de relaves basada en modelos, que puede ayudar a mejorar la precisión de las evaluaciones de riesgos y los planes de respuesta a emergencias.

Design of a pyrolyser model for the conversion of thermoplastics into fuels.

This study focuses on alternatives to the sustainable management of plastic wastes through the development of a pyrolyser model, adapted to the recycling of plastics into fuel. It is a batch reactor, fixed bed, designed and built for the extraction of pyrolysis oil that can be recycled into petrol or diesel. The pyrolyser consists of a reactor with a volume of 0.0424 m3 and a copper spiral condenser with 2.31 m length. The plastics used for this study were Low Density PolyEthylene (LDPE), polypropylene (PP), polystyrene (PS) and polyethylene terephthalate (PET). They were collected from surrounding companies, washed, sampled, cut and sieved. Two different sizes of pyrolysis material: 1-3 cm (G1) and 3-7 cm (G2) were obtained and tested. The pyrolysis reactor and the plastics entering at an ambient temperature of 25 °C were heated. Plastics were then melted at 110 °C and vaporised at 450 °C. The hot vapour produced circulated through a copper coil and condensed. The resulting liquid was called pyrolysis oil. The results of this study show that the pyrolysis of LDPE, PP and PS yields two liquids: the heavy and majority fraction which arelike the conventional diesel and the light fraction which is like gasoline. Yields of 6-12.4% for the light fraction and 43.2-63.8% corresponding to the heavy fraction are observed. PE has the highest yield, 63.8% for the heavy fraction and 12.4% for the light fraction. The study further underscores that the size of the pyrolysis material influences the yields, i.e. an increase of 12.5, 9.1 and 7 % for LDPE, PS and PP respectively when the size of the pyrolysis material is increased from G2 to G1. In contrast, the results of PET have shown a liquid that solidifies 46 s later. It was also noticed that 2061.34 kJ of energy was required to pyrolyse 1 kg of plastic and produce 0.762 l of fuel. The simple physico-chemical characterisation of the majority fraction shows a great similarity with diesel fuel, as the distillation went beyond 200 °C. Therefore, we can say that the diesel fraction is similar to diesel fuel. We equally observed a high cetane number (52.1-55.1) and a high calorific value (42.9-55.5 MJ/kg). Consequently, there are some points of non-conformity with the European 590 standard and Cameroonian specifications for diesel fuel. These include a low density (767.8-815.1 kg/m3) and a low viscosity at 40 °C (1.108-1.346 mm2/S). A thorough physico-chemical analysis will complete this study before any recommendation for appropriate use.

Tafenoquine following G6PD screening versus primaquine for the treatment of vivax malaria in Brazil: A cost-effectiveness analysis using a transmission model.

BACKGROUND: Malaria transmission modelling has demonstrated the potential impact of semiquantitative glucose-6-phosphate dehydrogenase (G6PD) testing and treatment with single-dose tafenoquine for Plasmodium vivax radical cure but has not investigated the associated costs. This study evaluated the cost-effectiveness of P. vivax treatment with tafenoquine after G6PD testing using a transmission model. METHODS AND FINDINGS: We explored the cost-effectiveness of using tafenoquine after G6PD screening as compared to usual practice (7-day low-dose primaquine (0.5 mg/kg/day) without G6PD screening) in Brazil using a 10-year time horizon with 5% discounting considering 4 scenarios: (1) tafenoquine for adults only assuming 66.7% primaquine treatment adherence; (2) tafenoquine for adults and children aged >2 years assuming 66.7% primaquine adherence; (3) tafenoquine for adults only assuming 90% primaquine adherence; and (4) tafenoquine for adults only assuming 30% primaquine adherence. The incremental cost-effectiveness ratios (ICERs) were estimated by dividing the incremental costs by the disability-adjusted life years (DALYs) averted. These were compared to a willingness to pay (WTP) threshold of US$7,800 for Brazil, and one-way and probabilistic sensitivity analyses were performed. All 4 scenarios were cost-effective in the base case analysis using this WTP threshold with ICERs ranging from US$154 to US$1,836. One-way sensitivity analyses showed that the results were most sensitive to severity and mortality due to vivax malaria, the lifetime and number of semiquantitative G6PD analysers needed, cost per malaria episode and per G6PD test strips, and life expectancy. All scenarios had a 100% likelihood of being cost-effective at the WTP threshold. The main limitations of this study are due to parameter uncertainty around our cost estimates for low transmission settings, the costs of G6PD screening, and the severity of vivax malaria. CONCLUSIONS: In our modelling study that incorporated impact on transmission, tafenoquine prescribed after a semiquantitative G6PD testing was highly likely to be cost-effective in Brazil. These results demonstrate the potential health and economic importance of ensuring safe and effective radical cure.

Cation Exchange and Spontaneous Crystal Repair Resulting in Ultrathin, Planar CdS Nanosheets.

Cation exchange has become a major postsynthetic tool to obtain nanocrystals with a combination of stoichiometry, size, and shape that is challenging to achieve by direct wet-chemical synthesis. Here, we report on the transformation of highly anisotropic, ultrathin, and planar PbS nanosheets into CdS nanosheets of the same dimensions. We monitor the evolution of the Cd-for-Pb exchange by ex-situ TEM, HAADF-STEM, and EDX. We observe that in the early stages of the exchange the sheets show large in-sheet voids that repair spontaneously upon further exchange and annealing, resulting in ultrathin, planar, and crystalline CdS nanosheets. After cation exchange, the nanosheets show broad sub-band gap luminescence, as often observed in CdS nanocrystals. The photoluminescence excitation spectrum reveals the heavy- and light-hole exciton features, with very strong quantum confinement and large electron-hole Coulomb energy, typical for 2D ultrathin Cd-chalcogenide nanosheets.

Methanotrophic potential of Dutch canal wall biofilms is driven by Methylomonadaceae.

Global urbanization of waterways over the past millennium has influenced microbial communities in these aquatic ecosystems. Increased nutrient inputs have turned most urban waters into net sources of the greenhouse gases carbon dioxide (CO2) and methane (CH4). Here, canal walls of five Dutch cities were studied for their biofilm CH4 oxidation potential, alongside field observations of water chemistry, and CO2 and CH4 emissions. Three cities showed canal wall biofilms with relatively high biological CH4 oxidation potential up to 0.48 mmol gDW-1 d-1, whereas the other two cities showed no oxidation potential. Salinity was identified as the main driver of biofilm bacterial community composition. Crenothrix and Methyloglobulus methanotrophs were observed in CH4-oxidizing biofilms. We show that microbial oxidation in canal biofilms is widespread and is likely driven by the same taxa found across cities with distinctly different canal water chemistry. The oxidation potential of the biofilms was not correlated with the amount of CH4 emitted but was related to the presence or absence of methanotrophs in the biofilms. This was controlled by whether there was enough CH4 present to sustain a methanotrophic community. These results demonstrate that canal wall biofilms can directly contribute to the mitigation of greenhouse gases from urban canals.

Differential Type-I Interferon Response in Buffy Coat Transcriptome of Individuals Infected with SARS-CoV-2 Gamma and Delta Variants.

The innate immune system is the first line of defense against pathogens such as the acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The type I-interferon (IFN) response activation during the initial steps of infection is essential to prevent viral replication and tissue damage. SARS-CoV and SARS-CoV-2 can inhibit this activation, and individuals with a dysregulated IFN-I response are more likely to develop severe disease. Several mutations in different variants of SARS-CoV-2 have shown the potential to interfere with the immune system. Here, we evaluated the buffy coat transcriptome of individuals infected with Gamma or Delta variants of SARS-CoV-2. The Delta transcriptome presents more genes enriched in the innate immune response and Gamma in the adaptive immune response. Interactome and enriched promoter analysis showed that Delta could activate the INF-I response more effectively than Gamma. Two mutations in the N protein and one in the nsp6 protein found exclusively in Gamma have already been described as inhibitors of the interferon response pathway. This indicates that the Gamma variant evolved to evade the IFN-I response. Accordingly, in this work, we showed one of the mechanisms that variants of SARS-CoV-2 can use to avoid or interfere with the host Immune system.

Notification of malaria cases in the Brazilian Amazon Basin from 2010 to 2020: an analysis of the reporting times.

BACKGROUND: As controlling malaria transmission remains a public-health challenge in the Brazilian Amazon basin, the National Surveillance System for Malaria (SIVEP-MALARIA) has registered malaria notifications for over fifteen years helping in the decision-making on control and elimination. As a surveillance database, the system is prone to reporting delays, and knowledge about reporting patterns is essential in decisions. METHODS: This study contains an analysis of temporal and state trends of reporting times in a total of 1,580,617 individual malaria reports from January 2010 to December 2020, applying procedures for statistical distribution fitting. A nowcasting technique was applied to show an estimation of number of cases using a statistical model of reporting delays. RESULTS: Reporting delays increased over time for the states of Amazonas, Rondônia, Roraima, and Pará. Amapá has maintained a similar reporting delay pattern, while Acre decreased reporting delay between 2010 and 2020. Predictions were more accurate in states with lower reporting delays. The temporal evolution of reporting delays only showed a decrease in malaria reports in Acre from 2010 to 2020. CONCLUSION: Malaria notifications may take days or weeks to enter the national surveillance database. The reporting times are likely to impact incidence estimation over periods when data is incomplete, whilst the impact of delays becomes smaller for retrospective analysis. Short-term assessments for the estimation of malaria incidence from the malaria control programme must deal with reporting delays.

Entomo-virological surveillance followed by serological active survey of symptomatic individuals is helpful to identify hotspots of early arbovirus transmission.

Arboviruses transmitted by Aedes aegypti in urban environments have spread rapidly worldwide, causing great impacts on public health. The development of reliable and timely alert signals is among the most important steps in designing accurate surveillance systems for vector-borne diseases. In July and September 2017, we conducted a pilot study to improve an existing integrated surveillance system by using entomo-virological surveillance to prioritize areas to conduct active searches for individuals with arbovirus infection symptoms. Foz do Iguaçu City has a permanent entomo-virological surveillance system with approximately 3,500 traps to capture Aedes sp. in the adult stage. The Aedes aegypti females are captured alive and human samples are submitted to RT-qPCR (real-time qPCR) screening for DENV, ZIKV, and CHIKV diagnosis. Of the 55 Ae. aegypti mosquitoes tested in July 2017, seven (12.7%) were considered positive for DENV-2 and three (5.4%) for CHIKV. In September, we tested a sample of 54 mosquitoes, and 15 (27.7%) were considered infected by DENV-2. We created 25 circumferences with 150-m radius each to perform an active survey to identify symptomatic householders. In July, we selected one circumference, and five (35.7%) patients were positive for DENV, whereas two (14.3%) for CHIKV. In September, we selected four circumferences, and, from the 21 individuals sampled, nine (42.8%) were positive for DENV-2. A statistical model with a binomial response was used to estimate the number of cases in areas without active surveys, i.e., 20 circumferences. We estimated an additional 83 symptomatic patients (95% CI: 45-145) to be found in active searches, with 38 (95% CI: 18-72) of them confirming arbovirus infection. Arbovirus detection and serotyping in mosquitoes, but also in symptomatic individuals during active surveys, can provide an alert signal of early arbovirus transmission.

Prevalence and predictors of anti-SARS-CoV-2 serology in a highly vulnerable population of Rio de Janeiro: A population-based serosurvey.

Background: COVID-19 serosurveys allow for the monitoring of the level of SARS-CoV-2 transmission and support data-driven decisions. We estimated the seroprevalence of anti-SARS-CoV-2 antibodies in a large favela complex in Rio de Janeiro, Brazil. Methods: A population-based panel study was conducted in Complexo de Manguinhos (16 favelas) with a probabilistic sampling of participants aged ≥1 year who were randomly selected from a census of individuals registered in primary health care clinics that serve the area. Participants answered a structured interview and provided blood samples for serology. Multilevel regression models (with random intercepts to account for participants' favela of residence) were used to assess factors associated with having anti-S IgG antibodies. Secondary analyses estimated seroprevalence using an additional anti-N IgG assay. Findings: 4,033 participants were included (from Sep/2020 to Feb/2021, 22 epidemic weeks), the median age was 39·8 years (IQR:21·8-57·7), 61% were female, 41% were mixed-race (Pardo) and 23% Black. Overall prevalence was 49·0% (95%CI:46·8%-51·2%) which varied across favelas (from 68·3% to 31·4%). Lower prevalence estimates were found when using the anti-N IgG assay. Odds of having anti-S IgG antibodies were highest for young adults, and those reporting larger household size, poor adherence to social distancing and use of public transportation. Interpretation: We found a significantly higher prevalence of anti-S IgG antibodies than initially anticipated. Disparities in estimates obtained using different serological assays highlight the need for cautious interpretation of serosurveys estimates given the heterogeneity of exposure in communities, loss of immunological biomarkers, serological antigen target, and variant-specific test affinity. Funding: Fundação Oswaldo Cruz, Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Fundação de Amparo a Pesquisa do Estado do Rio de Janeiro (FAPERJ), the European Union's Horizon 2020 research and innovation programme, Royal Society, Serrapilheira Institute, and FAPESP.

Citywide Integrated Aedes aegypti Mosquito Surveillance as Early Warning System for Arbovirus Transmission, Brazil.

Arbovirus epidemiology lacks efficient and timely surveillance systems with accurate outbreak alert signals. We devised a citywide integrated surveillance system combining entomologic, epidemiologic, and entomo-virologic data gathered during 2017-2020 in Foz do Iguaçu, Brazil. We installed 3,476 adult mosquito traps across the city and inspected traps every 2 months. We compared 5 entomologic indices: traditional house and Breteau indices for larval surveys and trap positivity, adult density, and mosquitoes per inhabitant indices for adult trapping. We screened for dengue, Zika, and chikungunya viruses in live adult Aedes aegypti mosquitoes collected from traps. Indices based on adult mosquito sampling had higher outbreak predictive values than larval indices, and we were able to build choropleth maps of infestation levels <36 h after each round of trap inspection. Locating naturally infected vectors provides a timely support tool for local public health managers to prioritize areas for intervention response to prevent virus outbreaks.

On multifactorial drivers for malaria rebound in Brazil: a spatio-temporal analysis.

BACKGROUND: Malaria incidence in Brazil reversed its decreasing trend when cases from recent years, as recent as 2015, exhibited an increase in the Brazilian Amazon basin, the area with the highest transmission of Plasmodium vivax and Plasmodium falciparum. In fact, an increase of more than 20% in the years 2016 and 2017 revealed possible vulnerabilities in the national malaria-control programme. METHODS: Factors potentially associated with this reversal, including migration, economic activities, and deforestation, were studied. Past incidences of malaria cases due to P. vivax and P. falciparum were analysed with a spatio-temporal Bayesian model using more than 5 million individual records of malaria cases from January of 2003 to December of 2018 in the Brazilian Amazon to establish the municipalities with unexpected increases in cases. RESULTS: Plasmodium vivax incidence surpassed the past trends in Amazonas (AM), Amapá (AP), Acre (AC), Pará (PA), Roraima (RR), and Rondônia (RO), implying a rebound of these states between 2015 and 2018. On the other hand, P. falciparum also surpassed the past trends in AM, AC, AP, and RR with less severity than P. vivax incidence. Outdoor activities, agricultural activities, accumulated deforestation, and travelling might explain the rebound in malaria cases in RR, AM, PA, and RO, mainly in P. vivax cases. These variables, however, did not explain the rebound of either P. vivax and P. falciparum cases in AC and AP states or P. falciparum cases in RR and RO states. CONCLUSION: The Amazon basin has experienced an unexpected increase in malaria cases, mainly in P. vivax cases, in some regions of the states of Amazonas, Acre, Pará, Amapá, Roraima, and Rondônia from 2015 to 2018 and agricultural activities, outdoor activities, travelling activities, and accumulated deforestation appear linked to this rebound of cases in particular regions with different impact. This shows the multifactorial effects and the heterogeneity of the Amazon basin, boosting the necessity of focusing the malaria control programme on particular social, economic, and environmental conditions.

Modelling policy combinations of vaccination and transmission suppression of SARS-CoV-2 in Rio de Janeiro, Brazil.

COVID-19 vaccination in Brazil required a phased program, with priorities for age groups, health workers, and vulnerable people. Social distancing and isolation interventions have been essential to mitigate the advance of the pandemic in several countries. We developed a mathematical model capable of capturing the dynamics of the SARS-CoV-2 dissemination aligned with social distancing, isolation measures, and vaccination. Surveillance data from the city of Rio de Janeiro provided a case study to analyze possible scenarios, including non-pharmaceutical interventions and vaccination in the epidemic scenario. Our results demonstrate that the combination of vaccination and policies of transmission suppression potentially lowered the number of hospitalized cases by 380+ and 66+ thousand cases, respectively, compared to an absence of such policies. On top of transmission suppression-only policies, vaccination impacted more than 230+ thousand averted hospitalized cases and 43+ thousand averted deaths. Therefore, health surveillance activities should be maintained along with vaccination planning in scheduled groups until a large vaccinated coverage is reached. Furthermore, this analytical framework enables evaluation of such scenarios.

Entomo-Virological Aedes aegypti Surveillance Applied for Prediction of Dengue Transmission: A Spatio-Temporal Modeling Study.

Currently, DENV transmitted primarily by Aedes aegypti affects approximately one in three people annually. The spatio-temporal heterogeneity of vector infestation and the intensity of arbovirus transmission require surveillance capable of predicting an outbreak. In this work, we used data from 4 years of reported dengue cases and entomological indicators of adult Aedes collected from approximately 3500 traps installed in the city of Foz do Iguaçu, Brazil, to evaluate the spatial and temporal association between vector infestation and the occurrence of dengue cases. Entomological (TPI, ADI and MII) and entomo-virological (EVI) indexes were generated with the goal to provide local health managers with a transmission risk stratification that allows targeting areas for vector control activities. We observed a dynamic pattern in the evaluation; however, it was a low spatio-temporal correlation of Ae. aegypti and incidence of dengue. Independent temporal and spatial effects capture a significant portion of the signal given by human arbovirus cases. The entomo-virological index (EVI) significantly signaled risk in a few areas, whereas entomological indexes were not effective in providing dengue risk alert. Investigating the variation of biotic and abiotic factors between areas with and without correlation should provide more information about the local epidemiology of dengue.

Effectiveness of Mass Vaccination in Brazil against Severe COVID-19 Cases

BackgroundMass vaccination campaigns started in Brazil on January/2021 with CoronaVac followed by ChAdOx1 nCov-19, and BNT162b2 mRNA vaccines. Target populations initially included vulnerable groups such as people older than 80 years, with comorbidities, of indigenous origin, and healthcare workers. Younger age groups were gradually included. MethodsA national cohort of 66.3 million records was compiled by linking registry-certified COVID-19 vaccination records from the Brazilian National Immunization Program with information on severe COVID-19 cases and deaths. Cases and deaths were aggregated by state and age group. Mixed-effects Poisson models were used to estimate the rate of severe cases and deaths among vaccinated and unvaccinated individuals, and the corresponding estimates of vaccine effectiveness by vaccine platform and age group. The study period is from mid-January to mid-July 2021. ResultsEstimates of vaccine effectiveness preventing deaths were highest at 97.9% (95% CrI: 93.5-99.8) among 20-39 years old with ChAdOx1 nCov-19, at 82.7% (95% CrI: 80.7-84.6) among 40-59 years old with CoronaVac, and at 89.9% (87.8--91.8) among 40-59 years old with partial immunization of BNT162b2. For all vaccines combined in the full regimen, the effectiveness preventing severe cases among individuals aged 80+ years old was 35.9% (95% CrI: 34.9-36.9) which is lower than that observed for individuals aged 60-79 years (61.0%, 95% CrI: 60.5-61.5). ConclusionDespite varying effectiveness estimates, Brazils population benefited from vaccination in preventing severe COVID-19 outcomes. Results, however, suggest significant vaccine-specific reductions in effectiveness by age, given by differences between age groups 60-79 years and over 80 years.