Network analysis of MERS coronavirus within households, communities, and hospitals to identify most centralized and super-spreading in the Arabian Peninsula, 2012 to 2016

Contact history is crucial during an infectious disease outbreak and vital when seeking to understand and predict the spread of infectious diseases in human populations. The transmission connectivity networks of people infected with highly contagious Middle East respiratory syndrome coronavirus (MERS-CoV) in Saudi Arabia were assessed to identify super-spreading events among the infected patients between 2012 and 2016. Of the 1379 MERS cases recorded during the study period, 321 (23.3%) cases were linked to hospital infection, out of which 203 (14.7%) cases occurred among healthcare workers. There were 1113 isolated cases while the number of recorded contacts per MERS patient is between 1 () and 17 (), with a mean of 0.27 (SD = 0.76). Five super-important nodes were identified based on their high number of connected contacts worthy of prioritization (at least degree of 5). The number of secondary cases in each SSE varies (range, 5–17). The eigenvector centrality was significantly () associated with place of exposure, with hospitals having on average significantly higher eigenvector centrality than other places of exposure. Results suggested that being a healthcare worker has a higher eigenvector centrality score on average than being nonhealthcare workers. Pathogenic droplets are easily transmitted within a confined area of hospitals; therefore, control measures should be put in place to curtail the number of hospital visitors and movements of nonessential staff within the healthcare facility with MERS cases

Individual and network characteristic associated with hospital-acquired Middle East Respiratory Syndrome coronavirus

Background: During outbreaks of infectious diseases, transmission of the pathogen can form networks of infected individuals connected either directly or indirectly. Methods: Network centrality metrics were used to characterize hospital-acquired Middle East Respiratory Syndrome Coronavirus (HA-MERS) outbreaks in the Kingdom of Saudi Arabia between 2012 and 2016. Covariate-adjusted multivariable logistic regression models were applied to assess the effect of individual level risk factors and network level metrics associated with increase in length of hospital stay and risk of deaths from MERS. Results: About 27% of MERS cases were hospital acquired during the study period. The median age of healthcare workers and hospitalized patients were 35 years and 63 years, respectively, Although HA-MERS were more connected, we found no significant difference in degree centrality metrics between HA-MERS and non-HA-MERS cases. Pre-existing medical conditions (adjusted Odds ratio (aOR) = 2.43, 95% confidence interval: (CI) [1.11–5.33]) and hospitalized patients (aOR = 29.99, 95% CI [1.80–48.65]) were the strongest risk predictors of death from MERS. The risk of death associated with 1-day increased length of stay was significantly higher for patients with comorbidities. Conclusion: Our investigation also revealed that patients with an HA-MERS infection experienced a significantly longer hospital stay and were more likely to die from the disease. Healthcare worker should be reminded of their potential role as hubs for pathogens because of their proximity to and regular interaction with infected patients. On the other hand, this study has shown that while healthcare workers acted as epidemic attenuators, hospitalized patients played the role of an epidemic amplifier

Statistical modelling of clustered and incomplete data with applications in population health studies in developing countries

Philosophiae Doctor - PhDThe United Nations (UN) Millennium Development Goals (MDGs) drafted eight goals to be achieved by the year 2015, namely: eradicating extreme poverty and hunger, achieving universal primary education, promoting gender equality and women empowerment, reducing child mortality, improving maternal health, combating HIV/AIDS, malaria and other diseases, ensuring environmental sustainability and lastly developing a global partnership for development. Many public health studies often result in complicated and complex data sets, the nature of these data sets could be clustered, multivariate, longitudinal, hierarchical, spatial, temporal or spatio-temporal. This often results in what is called correlated data, because the assumption of independence among observations may not be appropriate. The shared genetic traits in the studies of illness or shared household characteristics among family members in the studies of poverty are examples of correlated data. In cross-sectional studies, individuals may be nested within sub-clusters (e.g., families) that are nested within clusters (e.g., environment), thus causing correlation within clusters. Ignoring the structure of the data may result in asymptotically biased parameter estimates. Clustered data may also be a result of geographical location or time (spatial and temporal). A crucial step in modelling correlated data is the speci cation of the dependency by choosing the covariance/correlation function. However, often the choice for a particular application is unclear and diagnostic tests will have to be carried out, following tting of a model. This study's view of developing countries investigates the prospects of achieving MDGs through the development of flexible predictor statistical models. The first objective of this study is to explore the existing methods for modelling correlated data sets (hierarchical, multilevel and spatial) and then apply the methods in a novel way to several data sets addressing the underlying MDGs. One of the most challenging issue in spatial or spatio-temporal analysis is the choice of a valid and yet exible correlation (covariance) structure. In cases of high dimensionality of the data, where the number of spatial locations or time points that produced the observations is large, the analysis of such data presents great computational challenges. It is debatable whether some of the classical correlation structures adequately reect the dependency in the data. The second objective is to propose a new flexible technique for handling spatial, temporal and spatio-temporal correlations. The goal of this study is to resolve the dependencies problems by proposing a more robust method for modelling spatial correlation. The techniques are used for di erent correlation structures and then combined to form the resulting estimating equations using the platform of the Generalized Method of Moments. The proposed model will therefore be built on a foundation of the Generalized Estimating Equations; this has the advantage of producing consistent regression parameter estimates under mild conditions due to separation of the processes of estimating the regression parameters from the modelling of the correlation. These estimates of the regression parameters are consistent under mild conditions. Thirdly, to account for spatio-temporal correlation in data sets, a method that decouples the two sources of correlations is proposed. Speci cally, the spatial and temporal e ects were modelled separately and then combined optimally. The approach circumvents the need of inverting the full covariance matrix and simpli es the modelling of complex relationships such as anisotropy, which is known to be extremely di cult or Lastly, large public health data sets consist of a high degree of zero counts where it is very di cult to distinguish between "true zeros" and "imputed" zeros. This can be due to the reporting mechanism as a result of insecurity, technical and logistics issues. The focus is therefore on the implementation of a technique that is capable of handling such a problem. The study will make the assumption that "imputed" zeros are a random event and consider the option of discarding the zeros, and then model a conditional Poisson model, conditioning on all cases greater than 0

Spatial modelling of contribution of individual level risk factors for mortality from Middle East respiratory syndrome coronavirus in the Arabian Peninsula.

Middle East respiratory syndrome coronavirus is a contagious respiratory pathogen that is contracted via close contact with an infected subject. Transmission of the pathogen has occurred through animal-to-human contact at first followed by human-to-human contact within families and health care facilities. This study is based on a retrospective analysis of the Middle East respiratory syndrome coronavirus outbreak in the Kingdom of Saudi Arabia between June 2012 and July 2015. A Geoadditive variable model for binary outcomes was applied to account for both individual level risk factors as well spatial variation via a fully Bayesian approach. Out of 959 confirmed cases, 642 (67%) were males and 317 (33%) had died. Three hundred and sixty four (38%) cases occurred in Ar Riyad province, while 325 (34%) cases occurred in Makkah. Individuals with some comorbidity had a significantly higher likelihood of dying from MERS-CoV compared with those who did not suffer comorbidity [Odds ratio (OR) = 2.071; 95% confidence interval (CI): 1.307, 3.263]. Health-care workers were significantly less likely to die from the disease compared with non-health workers [OR = 0.372, 95% CI: 0.151, 0.827]. Patients who had fatal clinical experience and those with clinical and subclinical experiences were equally less likely to die from the disease compared with patients who did not have fatal clinical experience and those without clinical and subclinical experiences respectively. The odds of dying from the disease was found to increase as age increased beyond 25 years and was much higher for individuals with any underlying comorbidities. Interventions to minimize mortality from the Middle East respiratory syndrome coronavirus should particularly focus individuals with comorbidity, non-health-care workers, patients with no clinical fatal experience, and patients without any clinical and subclinical experiences.The authors received no specific funding for this work. All data analyzed in this study were publicly available

Does high public trust amplify compliance with stringent COVID-19 government health guidelines? A multi-country analysis using data from 102,627 individuals

Purpose To examine how public trust mediates the people’s adherence to levels of stringent government health policies and to establish if these effects vary across the political regimes. Methods This study utilizes data from two large-scale surveys: the global behaviors and perceptions at the onset of COVID-19 pandemic and the Oxford COVID-19 Government Response Tracker (OxCGRT). Linear regression models were used to estimate the effects of public trust and strictness of restriction measures on people’s compliance level. The model accounted for individual and daily variations in country-level stringency of preventative measures. Differences in the dynamics between public trust, the stringent level of government health guidelines and policy compliance were also examined among countries based on political regimes. Results We find strong evidence of the increase in compliance due to the imposition of stricter government restrictions. The examination of heterogeneous effects suggests that high public trust in government and the perception of its truthfulness double the impact of policy restrictions on public compliance. Among political regimes, higher levels of public trust significantly increase the predicted compliance as stringency level rises in authoritarian and democratic countries. Conclusion This study highlights the importance of public trust in government and its institutions during public health emergencies such as the COVID-19 pandemic. Our results are relevant and help understand why governments need to address the risks of non-compliance among low trusting individuals to achieve the success of the containment policies

Effects of time-lagged meteorological variables on attributable risk of leishmaniasis in central region of Afghanistan

Background: Leishmaniasis remains one of the world's most neglected vector-borne diseases, affecting predominantly poor communities mainly in developing countries. Previous studies have shown that the distribution and dynamics of leishmaniasis infections are sensitive to environmental factors, however, there are no studies on the burden of leishmaniasis attributable to time-varying meteorological variables. Methods: This study used data from 3 major leishmaniosis afflicted provinces of Afghanistan, between 2003 and 2009, to provide empirical analysis of change in heat/cold-leishmaniosis association. Non-linear and delayed exposure-lag-response relationship between meteorological variables and leishmaniasis were fitted with a distributed lag non-linear model applying a spline function which describes the dependency along the range of values with a lag of up to 12 months. We estimated the risk of leishmaniasis attributable to high and low temperature. Results: The median monthly mean temperature and rainfall were 16.1 °C and 0.6 in., respectively. Seasonal variations of leishmaniasis were consistent between males and females, however significant differences were observed among different age groups. Temperature effects were immediate and persistent (lag 0–12 months). The cumulative risks were highest at cold temperatures. The cumulative relative risks (logRR) for leishmaniasis were 6.16 (95% CI: 5.74–6.58) and 1.15 (95% CI: 1.32–1.31) associated with the 10th percentile temperature (2.16 °C) and the 90th percentile temperature (28.46 °C). The subgroup analysis showed increased risk for males as well as young and middle aged people at cold temperatures, however, higher risk was observed for the elderly in heat. The overall leishmaniasis-temperature attributable fractions was estimated to be 7.6% (95% CI: 7.5%–7.7%) and mostly due to cold. Conclusion: Findings in this study highlight the non-linearity, delay of effects and magnitude of leishmaniasis risk associated with temperature. The disparity of risk between different subgroups can hopefully advise policy makers and assist in leishmaniasis control program

Adverse birth outcomes due to exposure to household air pollution from unclean cooking fuel among women of reproductive age in Nigeria

Exposure to household air pollution (HAP) from cooking with unclean fuels and indoor smoking has become a significant contributor to global mortality and morbidity, especially in low- and middle-income countries such as Nigeria. Growing evidence suggests that exposure to HAP disproportionately affects mothers and children and can increase risks of adverse birth outcomes. We aimed to quantify the association between HAP and adverse birth outcomes of stillbirth, preterm births, and low birth weight while controlling for geographic variability. This study is based on a cross-sectional survey of 127,545 birth records from 41,821 individual women collected as part of the 2018 Nigeria Demographic and Health Survey (NDHS) covering 2013–2018. We developed Bayesian structured additive regression models based on Bayesian splines for adverse birth outcomes. Our model includes the mother’s level and household characteristics while correcting for spatial effects and multiple births per mother. Model parameters and inferences were based on a fully Bayesian approach via Markov Chain Monte Carlo (MCMC) simulations. We observe that unclean fuel is the primary source of cooking for 89.3% of the 41,821 surveyed women in the 2018 NDHS. Of all pregnancies, 14.9% resulted in at least one adverse birth outcome; 14.3% resulted in stillbirth, 7.3% resulted in an underweight birth, and 1% resulted in premature birth. We found that the risk of stillbirth is significantly higher for mothers using unclean cooking fuel. However, exposure to unclean fuel was not significantly associated with low birth weight and preterm birth. Mothers who attained at least primary education had reduced risk of stillbirth, while the risk of stillbirth increased with the increasing age of the mother. Mothers living in the Northern states had a significantly higher risk of adverse births outcomes in 2018. Our results show that decreasing national levels of adverse birth outcomes depends on working toward addressing the disparities between states

Multi-year trend analysis of childhood immunization uptake and coverage in Nigeria

As a leading indicator of child health, under-five mortality was incorporated in the United Nations Millennium Development Goals with the aim of reducing the rate by two-thirds between 1990 and 2015. Under-five mortality in Nigeria is alarmingly high, and many of the diseases that result in mortality are vaccine preventable. This study evaluates the uptake of childhood immunization in Nigeria from 1990 to 2008. A multi-year trend analysis was carried out using Alternating Logistic Regression on 46,130 children nested within 17,380 mothers in 1938 communities from the Nigerian Demographic and Health Surveys from 1990 to 2008. The findings reveal that mother-level and community-level variability are significantly associated with immunization uptake in Nigeria. The model also indicates that children delivered at private hospitals have a higher chance of being immunized than children who are delivered at home. Children from the poorest families (who are more likely to be delivered at home) have a lower chance of being immunized than those from the richest families (OR = 0.712; 95% CI, 0.641–0.792). Similarly, the chance of children with a mother with no education being immunized is decreased by 17% compared with children whose mother has at least a primary education. In the same way, children of mothers who are gainfully employed and those of older mothers have statistically significantly higher odds of being immunized. Children of households with a female head are less likely to be immunized than those from male-headed households. The statistical significance of the community–survey year interaction term suggests an increase in the odds of a child being immunized over the years and spread over communities. Evidence-based policy should lay more emphasis on mother- and community- level risk factors in order to increase immunization coverage among Nigerian children.Web of Scienc

An exploratory look at associated factors of poverty on educational attainment in Africa and in-depth multilevel modelling for Namibia.

This study examines several indicator variables related to education and poverty in Africa from the Demographic and Health Surveys (DHS). Many have described income and education as one of the fundamental determinants of health and as one of the indicators for socio-economic status. Firstly, data from thirty-six African countries were explored, geographical heterogeneity of the countries were discussed. Secondly, we carried out in-depth multi-level analyses using generating estimating equations on data for 72,230 respondents and from 5,436 households in the Namibia DHS (1992-2006). Results from statistical analyses indicate that age of household head, socioeconomic status of household, parent's level of education, family size and position of a child in the family play a significant role in the educational attainment of household members. We found that these household level characteristics are important predictors of educational attainment. Thus, government policy aimed at reducing household level poverty should be implemented to alleviate the economic power at household level thereby increasing educational attainmentDHE

Statistical Approaches to Infectious Diseases Modelling in Developing Countries: A Case of COVID-19

Essential skills required for both statistical consulting and collaboration are mostly informal and are rarely taught in the training institutions in developing countries. These critical skills constitute a significant missing gap and a major hindrance to the growth and development of capacity in statistics and data science practice in developing countries. The advent of LISA 2020 initiative is bridging this gap with a fast-growing network of “stat labs” spread across higher education institutions in Africa, India, Brazil and other parts of the world. This chapter will highlight how LISA 2020 Stat Labs (and other potential labs outside LISA 2020) engage in building capacity to improve informal statistical skills through training and collaborations. In addition, the chapter will review the activities and programs of the stat labs and the contributions being made to bring data science to bear on real-world problems. The chapter plans to draw out lessons that are unique and common to the different stat labs in the network