Call detail record aggregation methodology impacts infectious disease models informed by human mobility

This paper demonstrates how two different methods used to calculate population-level mobility from Call Detail Records (CDR) produce varying predictions of the spread of epidemics informed by these data. Our findings are based on one CDR dataset describing inter-district movement in Ghana in 2021, produced using two different aggregation methodologies. One methodology, "all pairs," is designed to retain long distance network connections while the other, "sequential" methodology is designed to accurately reflect the volume of travel between locations. We show how the choice of methodology feeds through models of human mobility to the predictions of a metapopulation SEIR model of disease transmission. We also show that this impact varies depending on the location of pathogen introduction and the transmissibility of infections. For central locations or highly transmissible diseases, we do not observe significant differences between aggregation methodologies on the predicted spread of disease. For less transmissible diseases or those introduced into remote locations, we find that the choice of aggregation methodology influences the speed of spatial spread as well as the size of the peak number of infections in individual districts. Our findings can help researchers and users of epidemiological models to understand how methodological choices at the level of model inputs may influence the results of models of infectious disease transmission, as well as the circumstances in which these choices do not alter model predictions

Low Level of Transmitted HIV Drug Resistance at Two HIV Care Centres in Ghana: A Threshold Survey

Background: As access to antiretroviral therapy (ART) increases, the emergence and transmission of HIV drug resistant strains becomes a major problem. The World Health Organization (WHO) therefore recommends an initial minimum-resource method to signal when transmitted HIV drug resistance (HIVDR) requires action.Objective: This survey sought to generate information on the presence of HIV drug-resistant strains in the locality where Ghana’s ART for HIV was first introduced.Methods: The Ghana HIVDR threshold survey (TS) was conducted and analyzed according to WHO strategy for surveillance of HIVDR in the Eastern Region of Ghana. Sixty (60) plasma specimens were collected from 2007 to 2009 by an unlinked anonymous method from HIV seropositive pregnant women, aged between 15 to24 years, who were with their first pregnancy and ART naive. Genotyping was done as follows; Ribonucleic acid (RNA) was extracted from the samples and the protease (PR) and reverse transcriptase (RT) genes amplified and sequenced. The sequences were then analyzed for HIV drug resistance mutations using Stanford University HIV Drug Resistance Database.Results: Only two individuals were found with major HIVDR mutations: one each in the PR and RT genes. Thus the level of HIVDR in the study population in 2009 was classified as low (< 5%).Conclusion: As at February 2009, transmitted drug resistance was not a serious problem in the Eastern Region of Ghana. However, it is important to continue monitoring tHIVDR in order to understand the dynamics of the evolution of HIV drug resistance in the country

Overview of preparedness and response to COVID-19 in Ghana

The Coronavirus disease 2019 (COVID-19) outbreak in Ghana is part of an ongoing pandemic caused by the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). The first two cases of COVID-19 were confirmed in Ghana on 12th March 2020. COVID-19 was consequently declared a Public Health Emergency of National Concern, triggering several response actions, including enhanced surveillance, case detection, case management and contact tracing, closure of borders, suspension of international flights, ban on social gatherings and closure of schools. Preparedness and response plans were activated for implementation at the national, regional, district and community levels. Ghana’s Strategic approaches were to limit and stop the importation of cases; detect and contain cases early; expand infrastructure, logistics and capacity to provide quality healthcare for the sick; minimise disruption to social and economic life and increase the domestic capacity of all sectors to deal with existing and future shocks. The health sector strategic frame focused on testing, treatment, and tracking. As of 31st December 2020, a total of 535,168 cases, including 335 deaths (CFR: 0.61%), have been confirmed with 53,928 recoveries and 905 active cases. All the regions have reported cases, with Greater Accra reporting the highest number. The response actions in Ghana have seen highlevel political commitment, appropriate and timely decisions, and a careful balance of public health interventions with economic and socio-cultural dynamics. Efforts are ongoing to intensify non-pharmaceutical interventions, sustain the gains made so far and introduce COVID-19 vaccines to reduce the public health burden of the disease in Ghan

Repurposing an integrated national influenza platform for genomic surveillance of SARS-CoV-2 in Ghana: a molecular epidemiological analysis

Background Genomic surveillance of SARS-CoV-2 is crucial for monitoring the spread of COVID-19 and guiding public health decisions, but the capacity for SARS-CoV-2 testing and sequencing in Africa is low. We integrated SARS-CoV-2 surveillance into an existing influenza surveillance network with the aim of providing insights into SARS-CoV-2 transmission and genomics in Ghana. Methods In this molecular epidemiological analysis, which is part of a wider multifaceted prospective observational study, we collected national SARS-CoV-2 test data from 35 sites across 16 regions in Ghana from Sept 1, 2020, to Nov 30, 2021, via the Ghanaian integrated influenza and SARS-CoV-2 surveillance network. SARS-CoV-2-positive samples collected through this integrated national influenza surveillance network and from international travellers arriving in Accra were sequenced with Oxford Nanopore Technology sequencing and the ARTIC tiled amplicon method. The sequence lineages were typed with pangolin and the phylogenetic analysis was conducted with IQ-Tree2 and TreeTime. Findings During the study period, 5495 samples were submitted for diagnostic testing through the national influenza surveillance network (2121 [46·1%] of 4021 samples with complete demographic data were from female individuals and 2479 [53·9%] of 4021 samples were from male individuals). We also obtained 2289 samples from travellers who arrived in Accra and had a positive lateral flow test, of whom 1626 (71·0%, 95% CI 69·1–72·9) were confirmed to be SARS-CoV-2 positive. Co-circulation of influenza and SARS-CoV-2 in Ghana was detected, with increased cases of influenza in November, 2020, November, 2021, and January and June, 2021. In 4124 samples from individuals with influenza-like illness, SARS-CoV-2 was identified in 583 (14·1%, 95% CI 13·1–15·2) samples and influenza in 356 (8·6%, 7·8–9·5). Conversely, in 476 samples from individuals with of severe acute respiratory illness, SARS-CoV-2 was detected in 58 (12·2%, 9·5–15·5) samples and influenza in 95 (19·9%, 16·5–23·9). We detected four waves of SARS-CoV-2 infections in Ghana; each wave was driven by a different variant: B.1 and B.1.1 were the most prevalent lineages in wave 1, alpha (B.1.1.7) was responsible for wave 2, delta (B.1.617.2) and its sublineages (closely related to delta genomes from India) were responsible for wave 3, and omicron variants were responsible for wave 4. We detected omicron variants among 47 (32%) of 145 samples from travellers during the start of the omicron spread in Ghana (wave 4). Interpretation This study shows the value of repurposing existing influenza surveillance platforms to monitor SARS-CoV-2. Influenza continued to circulate in Ghana in 2020 and 2021, and remained a major cause of severe acute respiratory illness. We detected importations of SARS-CoV-2 variants into Ghana, including those that did or did not lead to onward community transmission. Investment in strengthening national influenza surveillance platforms in low-income and middle-income countries has potential for ongoing monitoring of SARS-CoV-2 and future pandemics. Funding The EDCTP2 programme supported by the EU

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

Susceptibility of Anopheles Mosquito to Agricultural Insecticides in the Adansi North District, Ghana

This study determined the susceptibility of Anopheles mosquitoes to some agricultural insecticides used in the Adansi North District of the Ashanti Region, and the efficacy of Actellic (pirimiphos-methyl) 50 EC as an alternative insecticide for the control of mosquitoes in the district. Anopheleslarvae were collected from mosquito breeding sites near farms. Five insecticides were assayed, pirimiphos-methyl (0.9%), DDT (4%), propoxur (0.1%), deltamethrin (0.05%), and lambda-cyhalothrin (0.05%). The residual efficacy of pirimiphos-methyl 50 EC sprayed on two surfaces (mud and cement) were determined by cone bioassay test at two-weekly intervals for 15 weeks after spraying. The susceptibility test showed the levels of phenotypic resistance of Anopheles spp. to the agricultural insecticides. Anopheles gambiae s.l. (96.50%) was the most dominant Anopheles species. The principal malaria vector in the district was resistant to DDT, Propoxur, Deltamethrin, and Lambda-cyhalothrin. Pirimiphos-methyl an organophosphate remained effective against the malaria vector. Student t-test analysis of bioassay test results showed that statistically the average mortality of Anopheles mosquitoes on cement surface was higher than the average mortality on mud surface. In conclusion, agricultural insecticides used in the district were negatively affecting malaria vector control activities. The use of pirimiphos-methyl (Actellic 50 EC) as an alternative insecticide against the malaria vector was more effective on cemented wall surface than on mud surface wall

Call detail record aggregation methodology impacts infectious disease models informed by human mobility

This paper demonstrates how two different methods used to calculate population-level mobility from Call Detail Records (CDR) produce varying predictions of the spread of epidemics informed by these data. Our findings are based on one CDR dataset describing inter-district movement in Ghana in 2021, produced using two different aggregation methodologies. One methodology, "all pairs," is designed to retain long distance network connections while the other, "sequential" methodology is designed to accurately reflect the volume of travel between locations. We show how the choice of methodology feeds through models of human mobility to the predictions of a metapopulation SEIR model of disease transmission. We also show that this impact varies depending on the location of pathogen introduction and the transmissibility of infections. For central locations or highly transmissible diseases, we do not observe significant differences between aggregation methodologies on the predicted spread of disease. For less transmissible diseases or those introduced into remote locations, we find that the choice of aggregation methodology influences the speed of spatial spread as well as the size of the peak number of infections in individual districts. Our findings can help researchers and users of epidemiological models to understand how methodological choices at the level of model inputs may influence the results of models of infectious disease transmission, as well as the circumstances in which these choices do not alter model predictions.</p

Call detail record aggregation methodology impacts infectious disease models informed by human mobility.

This paper demonstrates how two different methods used to calculate population-level mobility from Call Detail Records (CDR) produce varying predictions of the spread of epidemics informed by these data. Our findings are based on one CDR dataset describing inter-district movement in Ghana in 2021, produced using two different aggregation methodologies. One methodology, "all pairs," is designed to retain long distance network connections while the other, "sequential" methodology is designed to accurately reflect the volume of travel between locations. We show how the choice of methodology feeds through models of human mobility to the predictions of a metapopulation SEIR model of disease transmission. We also show that this impact varies depending on the location of pathogen introduction and the transmissibility of infections. For central locations or highly transmissible diseases, we do not observe significant differences between aggregation methodologies on the predicted spread of disease. For less transmissible diseases or those introduced into remote locations, we find that the choice of aggregation methodology influences the speed of spatial spread as well as the size of the peak number of infections in individual districts. Our findings can help researchers and users of epidemiological models to understand how methodological choices at the level of model inputs may influence the results of models of infectious disease transmission, as well as the circumstances in which these choices do not alter model predictions

Association between mobility, non-pharmaceutical interventions, and COVID-19 transmission in Ghana: A modelling study using mobile phone data.

Governments around the world have implemented non-pharmaceutical interventions to limit the transmission of COVID-19. Here we assess if increasing NPI stringency was associated with a reduction in COVID-19 cases in Ghana. While lockdowns and physical distancing have proven effective for reducing COVID-19 transmission, there is still limited understanding of how NPI measures are reflected in indicators of human mobility. Further, there is a lack of understanding about how findings from high-income settings correspond to low and middle-income contexts. In this study, we assess the relationship between indicators of human mobility, NPIs, and estimates of Rt, a real-time measure of the intensity of COVID-19 transmission. We construct a multilevel generalised linear mixed model, combining local disease surveillance data from subnational districts of Ghana with the timing of NPIs and indicators of human mobility from Google and Vodafone Ghana. We observe a relationship between reductions in human mobility and decreases in Rt during the early stages of the COVID-19 epidemic in Ghana. We find that the strength of this relationship varies through time, decreasing after the most stringent period of interventions in the early epidemic. Our findings demonstrate how the association of NPI and mobility indicators with COVID-19 transmission may vary through time. Further, we demonstrate the utility of combining local disease surveillance data with large scale human mobility data to augment existing surveillance capacity to monitor the impact of NPI policies

Gut microbiota signature of pathogen-dependent dysbiosis in viral gastroenteritis.

Acute gastroenteritis associated with diarrhea is considered a serious disease in Africa and South Asia. In this study, we examined the trends in the causative pathogens of diarrhea and the corresponding gut microbiota in Ghana using microbiome analysis performed on diarrheic stools via 16S rRNA sequencing. In total, 80 patients with diarrhea and 34 healthy adults as controls, from 2017 to 2018, were enrolled in the study. Among the patients with diarrhea, 39 were norovirus-positive and 18 were rotavirus-positive. The analysis of species richness (Chao1) was lower in patients with diarrhea than that in controls. Beta-diversity analysis revealed significant differences between the two groups. Several diarrhea-related pathogens (e.g., Escherichia-Shigella, Klebsiella and Campylobacter) were detected in patients with diarrhea. Furthermore, co-infection with these pathogens and enteroviruses (e.g., norovirus and rotavirus) was observed in several cases. Levels of both Erysipelotrichaceae and Staphylococcaceae family markedly differed between norovirus-positive and -negative diarrheic stools, and the 10 predicted metabolic pathways, including the carbohydrate metabolism pathway, showed significant differences between rotavirus-positive patients with diarrhea and controls. This comparative study of diarrheal pathogens in Ghana revealed specific trends in the gut microbiota signature associated with diarrhea and that pathogen-dependent dysbiosis occurred in viral gastroenteritis