Leveraging human resources for outbreak analysis: lessons from an international collaboration to support the sub-Saharan African COVID-19 response.

Emerging infectious diseases are a growing threat in sub-Saharan African countries, but the human and technical capacity to quickly respond to outbreaks remains limited. Here, we describe the experience and lessons learned from a joint project with the WHO Regional Office for Africa (WHO AFRO) to support the sub-Saharan African COVID-19 response.In June 2020, WHO AFRO contracted a number of consultants to reinforce the COVID-19 response in member states by providing actionable epidemiological analysis. Given the urgency of the situation and the magnitude of work required, we recruited a worldwide network of field experts, academics and students in the areas of public health, data science and social science to support the effort. Most analyses were performed on a merged line list of COVID-19 cases using a reverse engineering model (line listing built using data extracted from national situation reports shared by countries with the Regional Office for Africa as per the IHR (2005) obligations). The data analysis platform The Renku Project ( https://renkulab.io ) provided secure data storage and permitted collaborative coding.Over a period of 6 months, 63 contributors from 32 nations (including 17 African countries) participated in the project. A total of 45 in-depth country-specific epidemiological reports and data quality reports were prepared for 28 countries. Spatial transmission and mortality risk indices were developed for 23 countries. Text and video-based training modules were developed to integrate and mentor new members. The team also began to develop EpiGraph Hub, a web application that automates the generation of reports similar to those we created, and includes more advanced data analyses features (e.g. mathematical models, geospatial analyses) to deliver real-time, actionable results to decision-makers.Within a short period, we implemented a global collaborative approach to health data management and analyses to advance national responses to health emergencies and outbreaks. The interdisciplinary team, the hands-on training and mentoring, and the participation of local researchers were key to the success of this initiative.

Decentralising scientific publishing: can the blockchain improve science communication?

We present a decentralised solution for managing scientific communication, based on distributed ledger technologies, also called blockchains. The proposed system aims to solve incentive problems displayed by traditional systems in scientific communication and publication. A minimal working model is presented, defining roles, processes, and expected results from the novel system. The proposed solution is viable, given the current status of blockchain technology, and should lead to a rethinking of current practices and their consequences for scientific communication.

Mathematical model of the dynamics of transmission and control of sporotrichosis in domestic cats.

Sporotrichosis is a subcutaneous mycosis with a global distribution, also known as "rose gardener's disease". Brazil is experiencing a rapid spread of the zoonotic transmission of of Sporothrix brasiliensis, the main etiological agent of this disease in this country, affecting domestic felines. Cost-effective interventions need to be developed to control this emergent public health problem. To allow for the comparison of alternative control strategies, we propose in this paper, a mathematical model representing the transmission of S. brasiliensis among cats, stratified by age and sex. Analytical properties of the model are derived and simulations show possible strategies for reducing the endemic levels of the disease in the cat population, with a positive impact on human health. The scenarios included mass treatment of infected cats and mass implementation of contact reduction practices, such as neutering. The results indicate that mass treatment can reduce substantially the disease prevalence, and this effect is potentialized when combined with neutering or other contact-reduction interventions. On the other hand, contact-reduction methods alone are not sufficient to reduce prevalence.

The effectiveness of COVID-19 vaccines against severe cases and deaths in Brazil from 2021 to 2022: a registry-based study.

Background: Brazil started the COVID-19 mass vaccination in January 2021 with CoronaVac and ChAdOx1, followed by BNT162b2 and Ad26.COV2.S vaccines. By the end of 2021, more than 317 million vaccine doses were administered in the adult population. This study aimed at estimating the effectiveness of the primary series of COVID-19 vaccination and booster shots in protecting against severe cases and deaths in Brazil during the first year of vaccination. Methods: A cohort dataset of over 158 million vaccination and severe cases records linked from official national registries was analyzed via a mixed-effects Poisson model, adjusted for age, state of residence, time after immunization, and calendar time to estimate the absolute vaccine effectiveness of the primary series of vaccination and the relative effectiveness of the booster. The method permitted analysis of effectiveness against hospitalizations and deaths, including in the periods of variant dominance. Findings: Vaccine effectiveness against severe cases and deaths remained over 25% and 50%, respectively, after 19 weeks from primary vaccination of BNT162b2, ChAdOx1, or CoronaVac vaccines. The boosters conferred greater protection than the primary series of vaccination, with heterologous boosters providing marginally greater protection than homologous. The effectiveness against hospitalization during the Omicron dominance in the 60+ years old population started at 61.7% (95% CI, 26.1-86.2) for ChAdOx1, 95.6% (95% CI, 82.4-99.9) for CoronaVac, and 72.3% (95% CI, 51.4-87.4) for the BNT162b2 vaccine. Interpretation: This study provides real-world evidence of the effectiveness of COVID-19 vaccination in Brazil, including during the Omicron wave, demonstrating protection even after waning effectiveness. Comparisons of the effectiveness among different vaccines require caution due to potential bias effects related to age groups, periods in the pandemic, and eventual behavioural changes. Funding: Fundação Oswaldo Cruz (FIOCRUZ), Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Fundação de Amparo a Pesquisa do Estado do Rio de Janeiro (FAPERJ), Pan American Health Organization (PAHO), Departamento de Ciência e Tecnologia da Secretaria de Ciência, Tecnologia, Inovação e Insumos Estratégicos em Saúde do Ministério da Saúde do Brasil (DECIT/SCTIE/MS).

COVID-19 in Switzerland real-time epidemiological analyses powered by EpiGraphHub.

Here we present the design and results of an analytical pipeline for COVID-19 data for Switzerland. It is applied to openly available data from the beginning of the epidemic in 2020 to the present day (august 2022). We analyzed the spatio-temporal patterns of the spread of SARS-CoV2 throughout the country, applying Bayesian inference to estimate population prevalence and hospitalization ratio. We also developed forecasting models to characterize the transmission dynamics for all the country's cantons taking into account their spatial correlations in COVID incidence. The two-week forecasts of new daily hospitalizations showed good accuracy, as reported herein. These analyses' raw data and live results are available on the open-source EpiGraphHub platform to support further studies.

Faster indicators of chikungunya incidence using Google searches.

Chikungunya, a mosquito-borne disease, is a growing threat in Brazil, where over 640,000 cases have been reported since 2017. However, there are often long delays between diagnoses of chikungunya cases and their entry in the national monitoring system, leaving policymakers without the up-to-date case count statistics they need. In contrast, weekly data on Google searches for chikungunya is available with no delay. Here, we analyse whether Google search data can help improve rapid estimates of chikungunya case counts in Rio de Janeiro, Brazil. We build on a Bayesian approach suitable for data that is subject to long and varied delays, and find that including Google search data reduces both model error and uncertainty. These improvements are largest during epidemics, which are particularly important periods for policymakers. Including Google search data in chikungunya surveillance systems may therefore help policymakers respond to future epidemics more quickly.

An in-depth statistical analysis of the COVID-19 pandemic's initial spread in the WHO African region.

During the first wave of the COVID-19 pandemic, sub-Saharan African countries experienced comparatively lower rates of SARS-CoV-2 infections and related deaths than in other parts of the world, the reasons for which remain unclear. Yet, there was also considerable variation between countries. Here, we explored potential drivers of this variation among 46 of the 47 WHO African region Member States in a cross-sectional study. We described five indicators of early COVID-19 spread and severity for each country as of 29 November 2020: delay in detection of the first case, length of the early epidemic growth period, cumulative and peak attack rates and crude case fatality ratio (CFR). We tested the influence of 13 pre-pandemic and pandemic response predictor variables on the country-level variation in the spread and severity indicators using multivariate statistics and regression analysis. We found that wealthier African countries, with larger tourism industries and older populations, had higher peak (p<0.001) and cumulative (p<0.001) attack rates, and lower CFRs (p=0.021). More urbanised countries also had higher attack rates (p<0.001 for both indicators). Countries applying more stringent early control policies experienced greater delay in detection of the first case (p<0.001), but the initial propagation of the virus was slower in relatively wealthy, touristic African countries (p=0.023). Careful and early implementation of strict government policies were likely pivotal to delaying the initial phase of the pandemic, but did not have much impact on other indicators of spread and severity. An over-reliance on disruptive containment measures in more resource-limited contexts is neither effective nor sustainable. We thus urge decision-makers to prioritise the reduction of resource-based health disparities, and surveillance and response capacities in particular, to ensure global resilience against future threats to public health and economic stability.

A Framework for Weather-Driven Dengue Virus Transmission Dynamics in Different Brazilian Regions.

This study investigated a model to assess the role of climate fluctuations on dengue (DENV) dynamics from 2010 to 2019 in four Brazilian municipalities. The proposed transmission model was based on a preexisting SEI-SIR model, but also incorporates the vector vertical transmission and the vector's egg compartment, thus allowing rainfall to be introduced to modulate egg-hatching. Temperature and rainfall satellite data throughout the decade were used as climatic model inputs. A sensitivity analysis was performed to understand the role of each parameter. The model-simulated scenario was compared to the observed dengue incidence and the findings indicate that the model was able to capture the observed seasonal dengue incidence pattern with good accuracy until 2016, although higher deviations were observed from 2016 to 2019. The results further demonstrate that vertical transmission fluctuations can affect attack transmission rates and patterns, suggesting the need to investigate the contribution of vertical transmission to dengue transmission dynamics in future assessments. The improved understanding of the relationship between different environment variables and dengue transmission achieved by the proposed model can contribute to public health policies regarding mosquito-borne diseases.

Identification of priority groups for COVID-19 vaccination in Brazil. / Identificação de grupos prioritários para a vacinação contra COVID-19 no Brasil.

In a context of community transmission and shortage of vaccines, COVID-19 vaccination should focus on directly reducing the morbidity and mortality caused by the disease. It was thus essential to define priority groups for vaccination by the Brazilian National Immunization Program (PNI in Portuguese), based on the risk of hospitalization and death from the disease. We calculated overrisk according to sex, age group, and comorbidities using hospitalization and death records from severe acute respiratory illness with confirmation of COVID-19 (SARI-COVID) in all of Brazil in the first 6 months of the epidemic. Higher overrisk was associated with male sex (hospitalization = 1.1 and death = 1.2), age over 45 years for hospitalization (OvRag ranging from 1.1 to 8.5), and age over 55 year for death (OvRag ranging from 1.5 to 18.3). In the groups with comorbidities, chronic kidney disease, diabetes mellitus, cardiovascular disease, and chronic lung disease were associated with overrisk, while there was no such evidence for asthma. Chronic kidney disease or diabetes and age over 60 showed an even stronger association, reaching overrisk of death 14 and 10 times greater than in the general population, respectively. For all the comorbidities, there was higher overrisk at older ages, with a downward gradient in the oldest age groups. This pattern was reversed when examining overrisk in the general population, for both hospitalization and death. The current study provided evidence of overrisk of hospitalization and death from SARI-COVID, assisting the definition of priority groups for COVID-19 vaccination.

Em um contexto de transmissão comunitária e escassez de vacinas, a vacinação contra a COVID-19 deve focar na redução direta da morbidade e da mortalidade causadas pela doença. Portanto, é fundamental a definição de grupos prioritários para a vacinação pelo Programa Nacional de Imunizações (PNI), baseada no risco de hospitalização e óbito pela doença. Para tal, calculamos o sobrerrisco por sexo, faixa etária e comorbidades por meio dos registros de hospitalização e óbito por síndrome respiratória aguda grave com confirmação de COVID-19 (SRAG-COVID) em todo o Brasil nos primeiros seis meses de epidemia. Apresentaram maior sobrerrisco pessoas do sexo masculino (hospitalização = 1,1 e óbito = 1,2), pessoas acima de 45 anos para hospitalização (SRfe variando de 1,1 a 8,5) e pessoas acima de 55 anos para óbitos (SRfe variando de 1,5 a 18,3). Nos grupos de comorbidades, doença renal crônica, diabetes mellitus, doença cardiovascular e pneumopatia crônica conferiram sobrerrisco, enquanto para asma não houve evidência. Ter doença renal crônica ou diabetes mellitus e 60 anos ou mais mostrou-se um fator ainda mais forte, alcançando sobrerrisco de óbito 14 e 10 vezes maior do que na população geral, respectivamente. Para todas as comorbidades, houve um sobrerrisco mais alto em idades maiores, com um gradiente de diminuição em faixas mais altas. Esse padrão se inverteu quando consideramos o sobrerrisco em relação à população geral, tanto para hospitalização quanto para óbito. O presente estudo forneceu evidências a respeito do sobrerrisco de hospitalização e óbito por SRAG-COVID, auxiliando na definição de grupos prioritários para a vacinação contra a COVID-19.

En un contexto de transmisión comunitaria y escasez de vacunas, la vacunación contra la COVID-19 debe enfocarse en la reducción directa de la morbilidad y de la mortalidad causadas por la enfermedad. Por lo tanto, es fundamental la definición de grupos prioritarios para la vacunación por el Programa Nacional de Inmunizaciones (PNI), basada en el riesgo de hospitalización y óbito por la enfermedad. Para tal fin, calculamos el sobrerriesgo por sexo, franja de edad y comorbilidades mediante los registros de hospitalización y óbito por síndrome respiratorio agudo grave con confirmación de COVID-19 (SRAG-COVID) en todo Brasil, durante los primeros seis meses de epidemia. Presentaron mayor sobrerriesgo personas del sexo masculino (hospitalización = 1,1 y óbito = 1,2), personas por encima de 45 años para hospitalización (SRfe variando de 1,1 a 8,5) y personas por encima de 55 años para óbitos (SRfe variando de 1,5 a 18,3). En los grupos de comorbilidades, enfermedad renal crónica, diabetes mellitus, enfermedad cardiovascular y neumopatía crónica ofrecieron sobrerriesgo, mientras que para el asma no hubo evidencia. Sufrir una enfermedad renal crónica o diabetes mellitus y tener 60 años o más mostró un factor todavía más fuerte, alcanzando sobrerriesgo de enfermedad 14 y 10 veces mayor que en la población general, respectivamente. Para todas las comorbilidades, hubo un sobrerriesgo más alto en edades mayores, con un gradiente de disminución en franjas más altas. Este patrón se invirtió cuando consideramos el sobrerriesgo en relación con la población general, tanto para hospitalización como para óbito. El presente estudio proporcionó evidencias respecto al sobrerriesgo de hospitalización y óbito por SRAG-COVID, ayudando en la definición de grupos prioritarios para la vacunación contra la COVID-19.

COVID-19 mortality in women and men in sub-Saharan Africa: a cross-sectional study.

INTRODUCTION: Since sex-based biological and gender factors influence COVID-19 mortality, we wanted to investigate the difference in mortality rates between women and men in sub-Saharan Africa (SSA). METHOD: We included 69 580 cases of COVID-19, stratified by sex (men: n=43 071; women: n=26 509) and age (0-39 years: n=41 682; 40-59 years: n=20 757; 60+ years: n=7141), from 20 member nations of the WHO African region until 1 September 2020. We computed the SSA-specific and country-specific case fatality rates (CFRs) and sex-specific CFR differences across various age groups, using a Bayesian approach. RESULTS: A total of 1656 deaths (2.4% of total cases reported) were reported, with men accounting for 70.5% of total deaths. In SSA, women had a lower CFR than men (mean [Formula: see text] = -0.9%; 95% credible intervals (CIs) -1.1% to -0.6%). The mean CFR estimates increased with age, with the sex-specific CFR differences being significant among those aged 40 years or more (40-59 age group: mean [Formula: see text] = -0.7%; 95% CI -1.1% to -0.2%; 60+ years age group: mean [Formula: see text] = -3.9%; 95% CI -5.3% to -2.4%). At the country level, 7 of the 20 SSA countries reported significantly lower CFRs among women than men overall. Moreover, corresponding to the age-specific datasets, significantly lower CFRs in women than men were observed in the 60+ years age group in seven countries and 40-59 years age group in one country. CONCLUSIONS: Sex and age are important predictors of COVID-19 mortality globally. Countries should prioritise the collection and use of sex-disaggregated data so as to design public health interventions and ensure that policies promote a gender-sensitive public health response.

Dynamic modeling of vaccinating behavior as a function of individual beliefs.

Individual perception of vaccine safety is an important factor in determining a person's adherence to a vaccination program and its consequences for disease control. This perception, or belief, about the safety of a given vaccine is not a static parameter but a variable subject to environmental influence. To complicate matters, perception of risk (or safety) does not correspond to actual risk. In this paper we propose a way to include the dynamics of such beliefs into a realistic epidemiological model, yielding a more complete depiction of the mechanisms underlying the unraveling of vaccination campaigns. The methodology proposed is based on Bayesian inference and can be extended to model more complex belief systems associated with decision models. We found the method is able to produce behaviors which approximate what has been observed in real vaccine and disease scare situations. The framework presented comprises a set of useful tools for an adequate quantitative representation of a common yet complex public-health issue. These tools include representation of beliefs as Bayesian probabilities, usage of logarithmic pooling to combine probability distributions representing opinions, and usage of natural conjugate priors to efficiently compute the Bayesian posterior. This approach allowed a comprehensive treatment of the uncertainty regarding vaccination behavior in a realistic epidemiological model.

Modeling Basins of Attraction for Breast Cancer Using Hopfield Networks.

Cancer is a genetic disease for which traditional treatments cause harmful side effects. After two decades of genomics technological breakthroughs, personalized medicine is being used to improve treatment outcomes and mitigate side effects. In mathematical modeling, it has been proposed that cancer matches an attractor in Waddington's epigenetic landscape. The use of Hopfield networks is an attractive modeling approach because it requires neither previous biological knowledge about protein-protein interactions nor kinetic parameters. In this report, Hopfield network modeling was used to analyze bulk RNA-Seq data of paired breast tumor and control samples from 70 patients. We characterized the control and tumor attractors with respect to their size and potential energy and correlated the Euclidean distances between the tumor samples and the control attractor with their corresponding clinical data. In addition, we developed a protocol that outlines the key genes involved in tumor state stability. We found that the tumor basin of attraction is larger than that of the control and that tumor samples are associated with a more substantial negative energy than control samples, which is in agreement with previous reports. Moreover, we found a negative correlation between the Euclidean distances from tumor samples to the control attractor and patient overall survival. The ascending order of each node's density in the weight matrix and the descending order of the number of patients that have the target active only in the tumor sample were the parameters that withdrew more tumor samples from the tumor basin of attraction with fewer gene inhibitions. The combinations of therapeutic targets were specific to each patient. We performed an initial validation through simulation of trastuzumab treatment effects in HER2+ breast cancer samples. For that, we built an energy landscape composed of single-cell and bulk RNA-Seq data from trastuzumab-treated and non-treated HER2+ samples. The trajectory from the non-treated bulk sample toward the treated bulk sample was inferred through the perturbation of differentially expressed genes between these samples. Among them, we characterized key genes involved in the trastuzumab response according to the literature.

Description and comparison of demographic characteristics and comorbidities in SARI from COVID-19, SARI from influenza, and the Brazilian general population. / SRAG por COVID-19 no Brasil: descrição e comparação de características demográficas e comorbidades com SRAG por influenza e com a população geral.

The study aims to describe patients hospitalized for severe acute respiratory illness (SARI) due to COVID-19 (SARI-COVID) in Brazil according to demographic characteristics and comorbidities up to the 21st Epidemiological Week of 2020. The study aimed to compare these characteristics with those of patients hospitalized for SARI due to influenza in 2019/2020 (SARI-FLU) and with the Brazilian general population. The proportions of demographic characteristics, comorbidities, and pregnant and postpartum women among patients hospitalized for SARI-COVID and SARI-FLU were obtained from the SIVEP-Gripe database, and the estimates for the Brazilian population were obtained from the population projections performed by Brazilian Institute of Geography and Statistics, Information System on Live Birth data, and nationwide surveys. Compared to the Brazilian population, patients hospitalized for SARI-COVID showed a higher proportion of males, elderly individuals and those aged 40 to 59 years, comorbidities (diabetes mellitus, cardiovascular disease, chronic kidney disease, and chronic lung diseases), and pregnant/postpartum women. Compared to the general population, Brazilians hospitalized for SARI-FLU showed higher prevalence rates of ages 0 to 4 years or over 60 years, white race/color, comorbidities (diabetes, chronic kidney disease, asthma, and other chronic lung diseases), and pregnant/postpartum women. The data suggest that these groups are evolving to more serious forms of the disease, so that longitudinal studies are extremely relevant for investigating this hypothesis and supporting appropriate public health policies.

Sex-Specific Asymmetrical Attack Rates in Combined Sexual-Vectorial Transmission Epidemics.

In 2015-2016, South America went through the largest Zika epidemic in recorded history. One important aspect of this epidemic was the importance of sexual transmission in combination with the usual vectorial transmission, with asymmetrical transmissibilities between sexual partners depending on the type of sexual contact; this asymmetry manifested itself in data as an increased risk to women. We propose a mathematical model for the transmission of the Zika virus including sexual transmission via all forms of sexual contact, as well as vector transmission, assuming a constant availability of mosquitoes. From this model, we derive an expression for R 0 , which is used to study and analyze the relative contributions of the male to female sexual transmission route vis-à-vis vectorial transmission. We also perform Bayesian inference of the model's parameters using data from the 2016 Zika epidemic in Rio de Janeiro.

Complete treatment of uncertainties in a model for dengue R0 estimation.

In real epidemic processes, the basic reproduction number R0 is the combined outcome of multiple probabilistic events. Nevertheless, it is frequently modeled as a deterministic function of epidemiological variables. This paper discusses the importance of adequate treatment of uncertainties in such models. This is done by comparing two methods of uncertainty analysis: Monte Carlo uncertainty analysis (MCUA) and the Bayesian melding (BM) method. These methods are applied to a model for the determination of R0 of dengue fever based on entomological parameters. The BM was shown to provide a complete treatment of the uncertainties associated with model parameters. In contrast to MCUA, the incorporation of uncertainties led to realistic posterior distributions for parameter and variables. The incorporation, by the BM, of all the available information, from observational data to expert opinions, allows for the constructive use of uncertainties generating informative posterior distributions for all of the model's components that are coherent as a set.

Estimating under-observation and the full size of the 2016 Zika epidemic in Rio de Janeiro.

The 2015-16 Zika epidemic spread quickly from north to south in Brazil. Two striking features were the much higher incidence in young adult women due to sexual transmission, and the serious congenital malformations and miscarriages associated to Zika infection in pregnant women. In this paper we use case reporting data along with live-birth records to reconstruct the full size of the epidemic through a Bayesian probabilistic graph model representing the Zika transmission probabilities of observation (case reporting) and of birth loss (through miscarriage or abortion). We find that the probability of observing (reporting) a Zika case is different between men and women and ranges between 10 to 13%. From these estimates we reconstruct the full size of the Zika epidemic in Rio de Janeiro in 2015-16.

Zika: an ongoing threat to women and infants.

Recent data from the municipality of Rio de Janeiro, Brazil, shows a sharp drop in the number of reported occurrences of Zika during the summer of 2016/2017, compared to the previous summer. There is still a much higher incidence among women than men, almost certainly due to sexual transmission. An unexpected feature of the new data is that there are proportionally far more cases affecting children under 15 months than older age classes. By comparing incidence rates in 2016/2017 and 2015/2016, we were able to deduce the proportion of reported cases affecting men and women, and verify that gender disparity is still present. Women and children are still risk groups for Zika infection, even during non-epidemic seasons.

Can Zika Account for the Missing Babies?

The Zika virus (ZIKV) spread rapidly in Brazil in 2015 and 2016. Rio de Janeiro was among the Brazilian cities which were hit the hardest, with more that a hundred thousand confirmed cases up to the end of 2016. Given the severity of the neurological damage caused by ZIKV on fetuses, we wondered whether it would also cause an increase in the number of miscarriages, especially very early ones. As early miscarriages are unlikely to be recorded as a health event, this effect-if it occurred-would only show up as a reduction in the number of live births. In this article, we show that there was a 15% drop in live births between September and December 2016 compared with the previous year, and that this sharp drop from epidemiological week 33 onward is strongly correlated with the number of recorded cases of Zika about 40 weeks earlier. We postulate that ZIKV is directly responsible for this drop in the birth rate. Further work is required to ascertain whether other factors such as the fear of having a microcephaly baby or the economic crisis are having a significant effect.