Author Correction: Modelling and predicting the spatio‑temporal spread of COVID‑19, associated deaths and impact of key risk factors in England

The original version of this Article contained an error in the Introduction where, “We utilise weekly MSOA level population mobility data against observed confirmed COVID-19 case data to assess the impact mobility reduction at small-area scale has had on case transmission, and counterfactually what the magnitude may have been under the scenario of no mobility loss.” now reads: “We utilise weekly Clinical Commission Group (CCG) level population mobility data against observed confirmed COVID-19 case data to assess the impact mobility reduction at small-area scale has had on case transmission, and counterfactually what the magnitude may have been under the scenario of no mobility loss.” In addition, in Table 2, the Spatial resolution was incorrectly given in the column “Model component”, rows “Covariate (daily varying)” and “Covariates (annual estimates, weekly fixed), Elderly population proportion living in deprivation”. The correct and incorrect values appear below. Incorrect: (Table presented.) Correct: (Table presented.) Finally, in the Methods section, under the subheadings ‘Data’, and ‘Data analysis”, “Daily population movement data by MSOA was extracted from the COVID-19 Impact monitor (https:// www. oxford- covid- 19. com/).” now reads: “Daily population movement data by Clinical Commission Group (CCG) was extracted from the COVID-19 Impact monitor (https:// www. oxford- covid- 19. com/). Weekly mobility in a given MSOA where assumed to be the same as the weekly mobility in the higher level CCG containing most of that MSOA.” “Here b = (b0, b1, b2) is the vector of regression coefficients for the intercept (representing the log-transformed baseline transmission rate across all locations), mobility represents the observed weekly mobility by MSOA;” now reads: “Here b = (b0, b1, b2) is the vector of regression coefficients for the intercept (representing the log-transformed baseline transmission rate across all locations), mobility represents the observed weekly mobility in a given MSOA based on the mobility in the CCG containing most of that MSOA;” The original Article has been corrected

Modelling and predicting the spatio-temporal spread of COVID-19, associated deaths and impact of key risk factors in England.

COVID-19 caseloads in England have passed through a first peak, and at the time of this analysis appeared to be gradually increasing, potentially signalling the emergence of a second wave. To ensure continued response to the epidemic is most effective, it is imperative to better understand both retrospectively and prospectively the geographical evolution of COVID-19 caseloads and deaths at small-area resolution, identify localised areas in space-time at significantly higher risk, quantify the impact of changes in localised population mobility (or movement) on caseloads, identify localised risk factors for increased mortality and project the likely course of the epidemic at high spatial resolution in coming weeks. We applied a Bayesian hierarchical space-time SEIR model to assess the spatiotemporal variability of COVID-19 caseloads (transmission) and deaths at small-area scale in England [Middle Layer Super Output Area (MSOA), 6791 units] and by week (using observed data from week 5 to 34 of 2020), including key determinants, the modelled transmission dynamics and spatial-temporal random effects. We also estimate the number of cases and deaths at small-area resolution with uncertainty projected forward in time by MSOA (up to week 51 of 2020), the impact mobility reductions (and subsequent easing) have had on COVID-19 caseloads and quantify the impact of key socio-demographic risk factors on COVID-19 related mortality risk by MSOA. Reductions in population mobility during the course of the first lockdown had a significant impact on the reduction of COVID-19 caseloads across England, however local authorities have had a varied rate of reduction in population movement which our model suggest has substantially impacted the geographic heterogeneity in caseloads at small-area scale. The steady gain in population mobility, observed from late April, appears to have contributed to a slowdown in caseload reductions towards late June and subsequent start of the second wave. MSOA with higher proportions of elderly (70+ years of age) and elderly living in deprivation, both with very distinct geographic distributions, have a significantly elevated COVID-19 mortality rates. While non-pharmaceutical interventions (that is, reductions in population mobility and social distancing) had a profound impact on the trajectory of the first wave of the COVID-19 outbreak in England, increased population mobility appears to have significantly contributed to the second wave. A number of contiguous small-areas appear to be at a significant elevated risk of high COVID-19 transmission, many of which are also at increased risk for higher mortality rates. A geographically staggered re-introduction of intensified social distancing measures is advised and limited cross MSOA movement if the magnitude and geographic extent of the second wave is to be reduced

Associations between school- and household-level water, sanitation and hygiene conditions and soil-transmitted helminth infection among Kenyan school children.

BACKGROUND: Soil-transmitted helminths, a class of parasitic intestinal worms, are pervasive in many low-income settings. Infection among children can lead to poor nutritional outcomes, anaemia, and reduced cognition. Mass treatment, typically administered through schools, with yearly or biannual drugs is inexpensive and can reduce worm burden, but reinfection can occur rapidly. Access to and use of sanitation facilities and proper hygiene can reduce infection, but rigorous data are scarce. Among school-age children, infection can occur at home or at school, but little is known about the relative importance of WASH in transmission in these two settings. METHODS: We explored the relationships between school and household water, sanitation, and hygiene conditions and behaviours during the baseline of a large-scale mass drug administration programme in Kenya. We assessed several WASH measures to quantify the exposure of school children, and developed theory and empirically-based parsimonious models. RESULTS: Results suggest mixed impacts of household and school WASH on prevalence and intensity of infection. WASH risk factors differed across individual worm species, which is expected given the different mechanisms of infection. CONCLUSIONS: No trend of the relative importance of school versus household-level WASH emerged, though some factors, like water supply were more strongly related to lower infection, which suggests it is important in supporting other school practices, such as hand-washing and keeping school toilets clean

Identifying co-endemic areas for major filarial infections in sub-Saharan Africa: seeking synergies and preventing severe adverse events during mass drug administration campaigns

BACKGROUND: Onchocerciasis and lymphatic filariasis (LF) are major filarial infections targeted for elimination in most endemic sub-Saharan Africa (SSA) countries by 2020/2025. The current control strategies are built upon community-directed mass administration of ivermectin (CDTI) for onchocerciasis, and ivermectin plus albendazole for LF, with evidence pointing towards the potential for novel drug regimens. When distributing microfilaricides however, considerable care is needed to minimise the risk of severe adverse events (SAEs) in areas that are co-endemic for onchocerciasis or LF and loiasis. This work aims to combine previously published predictive risk maps for onchocerciasis, LF and loiasis to (i) explore the scale of spatial heterogeneity in co-distributions, (ii) delineate target populations for different treatment strategies, and (iii) quantify populations at risk of SAEs across the continent. METHODS: Geographical co-endemicity of filarial infections prior to the implementation of large-scale mass treatment interventions was analysed by combining a contemporary LF endemicity map with predictive prevalence maps of onchocerciasis and loiasis. Potential treatment strategies were geographically delineated according to the level of co-endemicity and estimated transmission intensity. RESULTS: In total, an estimated 251 million people live in areas of LF and/or onchocerciasis transmission in SSA, based on 2015 population estimates. Of these, 96 million live in areas co-endemic for both LF and onchocerciasis, providing opportunities for integrated control programmes, and 83 million live in LF-monoendemic areas potentially targetable for the novel ivermectin-diethylcarbamazine-albendazole (IDA) triple therapy. Only 4% of the at-risk population live in areas co-endemic with high loiasis transmission, representing up to 1.2 million individuals at high risk of experiencing SAEs if treated with ivermectin. In these areas, alternative treatment strategies should be explored, including biannual albendazole monotherapy for LF (1.4 million individuals) and 'test-and-treat' strategies (8.7 million individuals) for onchocerciasis. CONCLUSIONS: These maps are intended to initiate discussion around the potential for tailored treatment strategies, and highlight populations at risk of SAEs. Further work is required to test and refine strategies in programmatic settings, providing the empirical evidence needed to guide efforts towards the 2020/2025 goals and beyond

Spatial distribution of podoconiosis in relation to environmental factors in Ethiopia: a historical review

BACKGROUND An up-to-date and reliable map of podoconiosis is needed to design geographically targeted and cost-effective intervention in Ethiopia. Identifying the ecological correlates of the distribution of podoconiosis is the first step for distribution and risk maps. The objective of this study was to investigate the spatial distribution and ecological correlates of podoconiosis using historical and contemporary survey data. METHODS Data on the observed prevalence of podoconiosis were abstracted from published and unpublished literature into a standardized database, according to strict inclusion and exclusion criteria. In total, 10 studies conducted between 1969 and 2012 were included, and data were available for 401,674 individuals older than 15 years of age from 229 locations. A range of high resolution environmental factors were investigated to determine their association with podoconiosis prevalence, using logistic regression. RESULTS The prevalence of podoconiosis in Ethiopia was estimated at 3.4% (95% CI 3.3%-3.4%) with marked regional variation. We identified significant associations between mean annual Land Surface Temperature (LST), mean annual precipitation, topography of the land and fine soil texture and high prevalence of podoconiosis. The derived maps indicate both widespread occurrence of podoconiosis and a marked variability in prevalence of podoconiosis, with prevalence typically highest at altitudes >1500 m above sea level (masl), with >1500 mm annual rainfall and mean annual LST of 19-21°C. No (or very little) podoconiosis occurred at altitudes 24°C. CONCLUSION Podoconiosis remains a public health problem in Ethiopia over considerable areas of the country, but exhibits marked geographical variation associated in part with key environmental factors. This is work in progress and the results presented here will be refined in future work

Prevalence of Plasmodium falciparum infection in asymptomatic rural Gabonese populations

<p>Abstract</p> <p>Background</p> <p>Malaria may be perennial or epidemic in sub-Saharan Africa, and its transmission may be stable or unstable, depending on the region. The prevalence of asymptomatic <it>Plasmodium falciparum </it>carriage is poorly documented in Gabon. A large survey of <it>P. falciparum </it>infection was conducted in asymptomatic individuals living in rural Gabon.</p> <p>Methods</p> <p>Two hundred and twenty-two villages were randomly selected in the nine administrative regions. With the participants' informed consent, blood samples were collected for thick and thin blood film examination after 20% Giemsa staining. Prevalence rates were calculated per village, per region and per ecosystem, and nationwide. Demographic risk factors were identified with STATA software version 9.0. Significance was assumed at p < 0.05.</p> <p>Results and discussion</p> <p>The prevalence of <it>P. falciparum </it>in adults was 6.2% (269/4342) nationwide, with a maximum of 37.2% in one village; a linear decrease was observed with increasing age (p = 0.045). Only 5% of the 399 children from forest areas tested positive. The prevalence was significantly higher in forest areas (7%) than in savannah (4%) and lakeland (2.5%). Within the forest region, the prevalence was significantly higher in forest grassland (10.9%) than in the mountain forest (3.5%), interior forest (6.8%) and north-eastern forest (4.5%).</p> <p>Conclusion</p> <p><it>Plasmodium falciparum </it>carriage remains high among adults in rural Gabon. Control measures must be adapted to the region and ecosystem. Routine treatment of asymptomatic individuals should be considered.</p

Multi-parallel qPCR provides increased sensitivity and diagnostic breadth for gastrointestinal parasites of humans: field-based inferences on the impact of mass deworming

BACKGROUND: Although chronic morbidity in humans from soil transmitted helminth (STH) infections can be reduced by anthelmintic treatment, inconsistent diagnostic tools make it difficult to reliably measure the impact of deworming programs and often miss light helminth infections. METHODS: Cryopreserved stool samples from 796 people (aged 2-81 years) in four villages in Bungoma County, western Kenya, were assessed using multi-parallel qPCR for 8 parasites and compared to point-of-contact assessments of the same stools by the 2-stool 2-slide Kato-Katz (KK) method. All subjects were treated with albendazole and all Ascaris lumbricoides expelled post-treatment were collected. Three months later, samples from 633 of these people were re-assessed by both qPCR and KK, re-treated with albendazole and the expelled worms collected. RESULTS: Baseline prevalence by qPCR (n = 796) was 17 % for A. lumbricoides, 18 % for Necator americanus, 41 % for Giardia lamblia and 15% for Entamoeba histolytica. The prevalence was <1% for Trichuris trichiura, Ancylostoma duodenale, Strongyloides stercoralis and Cryptosporidium parvum. The sensitivity of qPCR was 98% for A. lumbricoides and N. americanus, whereas KK sensitivity was 70% and 32%, respectively. Furthermore, qPCR detected infections with T. trichiura and S. stercoralis that were missed by KK, and infections with G. lamblia and E. histolytica that cannot be detected by KK. Infection intensities measured by qPCR and by KK were correlated for A. lumbricoides (r = 0.83, p < 0.0001) and N. americanus (r = 0.55, p < 0.0001). The number of A. lumbricoides worms expelled was correlated (p < 0.0001) with both the KK (r = 0.63) and qPCR intensity measurements (r = 0.60). CONCLUSIONS: KK may be an inadequate tool for stool-based surveillance in areas where hookworm or Strongyloides are common or where intensity of helminth infection is low after repeated rounds of chemotherapy. Because deworming programs need to distinguish between populations where parasitic infection is controlled and those where further treatment is required, multi-parallel qPCR (or similar high throughput molecular diagnostics) may provide new and important diagnostic information