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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. Author summary: Predicting the sub-national spread of infectious disease requires accurate measurements of inter-regional travel networks. Often, this information is derived from the patterns of mobile device connections to the cellular network. This travel data is then used as an input to epidemiological models of infection transmission, defining the likelihood that disease is "exported" between regions. In this paper, we use one mobile device dataset collected in Ghana in 2021, aggregated according to two different methodologies which represent different aspects of inter-regional travel. We show how the choice of aggregation methodology leads to different predicted epidemics, and highlight the conditions under which models of infection transmission may be influenced by methodological choices in the aggregation of travel data used to parameterize these models. For example, we show how aggregation methodology changes predicted epidemics for less-transmissible infections and under certain models of human movement. We also highlight areas of relative stability, where aggregation choices do not alter predicted epidemics, such as cases where an infection is highly transmissible or is introduced into a central location. [ABSTRACT FROM AUTHOR]

Introduction: Health care workers (HCWs) are crucial to the fight against COVID-19 and are at risk of being infected. We sought to determine the risk factors and associations of COVID-19 among HCWs in Ghana during the period of the pandemic. Materials and methods: A case-control study was conducted using the WHO COVID-19 HCWs exposure risk assessment tool. A HCW was categorized as "high risk" for COVID-19 if s/he did not respond "always, as recommended" to adherence to Infection Prevention and Control (IPC) measures during a healthcare interaction. A HCW was categorized as "low risk" if s/he responded "always, as recommended" to adherence to IPC measures. We used univariate and multiple logistic regression models to determine associated risk factors. Statistical significance was set at 5%. Results: A total of 2402 HCWs were recruited and the mean age was 33.2±7.1 years. Almost 87% (1525/1745) of HCWs had high risk for COVID-19 infection. Risk factors identified were profession (doctor- aOR: 2.13, 95%CI: 1.54–2.94; radiographer—aOR: 1.16, 95% CI: 0.44–3.09)), presence of comorbidity (aOR: 1.89, 95%CI: 1.29–2.78), community exposure to virus (aOR: 1.26, 95% CI: 1.03–1.55), not performing hand hygiene before and after aseptic procedures performed (aOR: 1.6, 95% CI: 1.05–2.45); not frequently decontaminating high-touch surfaces always as recommended (aOR: 2.31, 95%CI: 1.65–3.22; p = 0.001) and contact with a confirmed COVID-19 patient (aOR: 1.39, 95% CI: 1.15–1.67). Among those who came into any form of contact with confirmed COVID-19 patient, providing direct care (aOR: 2.0, 95%CI: 1.36–2.94), face-to-face contact (aOR: 2.23, 95%CI: 1.41–3.51), contact with environment/materials used by COVID-19 patient (aOR: 2.25, 95%CI: 1.45–3.49) and presence during conduct of aerosol generating procedures (aOR: 2.73, 95%CI: 1.74–4.28) were associated with COVID-19 infection. Conclusion: Non-adherence to IPC guidelines puts HCWs at increased risk of COVID-19 infection thus ensuring IPC adherence is key to reducing this risk. [ABSTRACT FROM AUTHOR]

Introduction: Ghana is the first country in sub-Saharan Africa (SSA) to aim for universal health coverage (UHC). Based on Ghana's UHC system, the accessibility and distribution of healthcare facilities were evaluated for 2020. Projecting into 2030, this study aimed at providing geographical information data for guiding future policies on siting required healthcare facilities. Ghana as a precedent for SSA was evaluated and proposed to "leave no disease behind" in the surveillance of infectious diseases (IDs). This is to reinforce the sustainable development goals (SDG) 3 agenda on health that underpins monitoring equity in "leaving no one behind." Methods: Geospatial accessibility, travel time data, and algorithms were employed to evaluate the universality and accessibility of healthcare facilities, and their future projections to meet UHC by 2030. Healthcare facilities as surveillance sites were compared to community-based surveillance to identify which would be more applicable as a surveillance system to leave no disease behind in Ghana. Findings: Ghana has 93.8%, 6.1% and 0.1% as primary, secondary and tertiary healthcare facilities respectively. It has 26.1% of healthcare facilities remaining to meet the SDG 3 health target by 2030. In terms of providing quality healthcare, 29.3% and 67.2% of the additional required healthcare facilities for optimal allocation and achieving the UHC target need to be secondary and tertiary respectively. In assessing the broad spectrum of IDs studied from 2000 to 2020, an average of 226 IDs were endemic or potentially endemic to Ghana. The majority of the studies carried out to identify these IDs were done through community-based surveillance. Conclusion: Establishing community-based surveillance sites to leave no disease behind and also providing the required healthcare facilities to reinforce leaving no one behind will enhance the universal health security of Ghana as a precedent for SSA. [ABSTRACT FROM AUTHOR]

Background: The declaration of COVID-19 as a pandemic on March 11 2020, by the World Health Organisation prompted the need for a sustained and a rapid international response. In a swift response, the Government of Ghana, in partnership with Zipline company, launched the use of Unmanned Automated Vehicles (UAV) to transport suspected samples from selected districts to two foremost testing centres in the country. Here, we present the experiences of employing this technology and its impact on the transport time to the second largest testing centre, the Kumasi Centre for Collaborative Research in Tropical Medicine (KCCR) in Kumasi, Ghana. Methods: Swab samples collected from suspected COVID-19 patients were transported to the Zipline office by health workers. Information on the samples were sent to laboratory personnel located at KCCR through a WhatsApp platform to get them ready to receive the suspected COVID-19 samples while Zipline repackaged samples and transported them via drone. Time of take-off was reported as well as time of drop-off. Results: A total of 2537 COVID-19 suspected samples were received via drone transport from 10 districts between April 2020 to June 2021 in 440 deliveries. Ejura-Sekyedumase District Health Directorate delivered the highest number of samples (765; 30%). The farthest district to use the drone was Pru East, located 270 km away from KCCR in Kumasi and 173 km to the Zipline office in Mampong. Here, significantly, it took on the average 39 minutes for drones to deliver samples compared to 117 minutes spent in transporting samples by road (p<0.001). Conclusion: The use of drones for sample transport during the COVID-19 pandemic significantly reduced the travel time taken for samples to be transported by road to the testing site. This has enhanced innovative measures to fight the pandemic using technology. [ABSTRACT FROM AUTHOR]

Yellow fever is endemic in Ghana and outbreaks occur periodically. The prodromal signs due to Yellow Fever Virus (YFV) infection are non-specific, making clinical signs unreliable as the sole criteria for diagnosis. Accurate laboratory confirmation of suspected yellow fever cases is therefore vital in surveillance programs. Reporting of ELISA IgM testing results by laboratories can delay due to late arrival of samples from the collection sites as well as limited availability of ELISA kits. In this study, the diagnostic performance characteristics of a rapid immunochromatographic Standard Q Yellow Fever IgM test kit (SD Biosensor) was evaluated for the rapid diagnosis of Yellow Fever infection in Ghana. A panel of 275 sera, comprising 81 confirmed YFV positives and 194 negatives were re-tested in this study using the Standard Q Yellow Fever IgM test kit. Using the CDC/WHO Yellow Fever IgM capture ELISA as a benchmark, the sensitivity, specificity and accuracy of the Standard Q Yellow Fever test kit were 96.3%, 97.9% and 97.5%, respectively. The false positivity rate was 5.1% and there was no cross-reactivity when the Standard Q Yellow Fever test kit was tested against dengue, malaria and hepatitis B and C positive samples. In addition, inter-reader variability and invalid rate were both zero. The results indicate that the diagnostic performance of the Standard Q Yellow Fever IgM test kit on serum or plasma is comparable to the serum IgM detection by ELISA and can be used as a point of care rapid diagnostic test kit for YFV infection in endemic areas. [ABSTRACT FROM AUTHOR]

Ghana Health Service (GHS) has strengthened community-based surveillance (CBS) to facilitate early detection and rapid reporting of health events of all origins. Since June 2017, GHS has employed an event-based surveillance approach at the community level in a phased manner. CBS coverage has broadened from 2 to 30 districts across Ghana. Through this effort, capacity was built across all administrative levels in these districts to detect, report, triage, and verify signals, and to perform risk assessment and investigate events. Data were collected and analyzed during an evaluation of initial 2-district implementation in March 2018 and during expanded 30-district implementation in March 2019. Between September 2018 and March 2019, 317 health events were detected through CBS. These events included vaccine-preventable disease cases, acute hemorrhagic conjunctivitis outbreaks, clusters of unexpected animal deaths, and foodborne illness clusters. Eighty-nine percent of the 317 events were reported to district-level public health staff within 24 hours of detection at the community level, and 87% of all detected events were responded to within 48 hours of detection. CBS detected 26% of all suspected vaccine-preventable disease cases that were reported from implementing districts through routine disease surveillance. GHS strengthened CBS in Ghana to function as an early warning system for health events of all origins, advancing the Global Health Security Agenda. [ABSTRACT FROM AUTHOR]

Dengue fever is known to be one of the most common arthropod-borne viral infectious diseases of public health importance. The disease is now endemic in more than 100 countries in Africa, the Americas, the Eastern Mediterranean, Southeast Asia and the Western Pacific with an estimated two fifths of the world's population being at risk. The notable endemic viral hemorrhagic fevers (VHFs) found in West Africa, including yellow fever, Lassa fever, Rift Valley fever, dengue fever and until recently Ebola have been responsible for most outbreaks with fatal consequences. These VHFs usually produce unclear acute febrile illness, especially in the acute phase of infection. In this study we detected the presence of 2 different serotypes (DENV-2 and DENV-3) of Dengue virus in 4 sera of 150 patients clinically suspected of Ebola virus disease during the Ebola Virus Disease (EVD) outbreak in West Africa with the use of serological and molecular test assays. Sequence data was successfully generated for DENV-3 and phylogenetic analysis of the envelope gene showed that the DENV-3 sequences had close homology with DENV-3 sequences from Senegal and India. This study documents molecular evidence of an indigenous Dengue fever viral infection in Ghana and therefore necessitates the need to have an efficient surveillance system to rapidly detect and control the dissemination of the different serotypes in the population which has the potential to cause outbreaks of dengue hemorrhagic fevers. [ABSTRACT FROM AUTHOR]

Background: Increases in pneumococcal meningitis were reported from Ghanaian regions that lie in the meningitis belt in 2016–2017, despite introduction of 13-valent pneumococcal conjugate vaccine (PCV13) in 2012 using a 3-dose schedule (6, 10, and 14 weeks). We describe pneumococcal meningitis epidemiology in the Ghanaian Northern and Upper West regions across two meningitis seasons. Methods: Suspected meningitis cases were identified using World Health Organization standard definitions. Pneumococcal meningitis was confirmed if pneumococcus was the sole pathogen detected by polymerase chain reaction, culture, or latex agglutination in cerebrospinal fluid collected from a person with suspected meningitis during December 2015-March 2017. Pneumococcal serotyping was done using PCR. Annual age-specific pneumococcal meningitis incidence (cases per 100,000 population) was calculated, adjusting for suspected meningitis cases lacking confirmatory testing. Findings: Among 153 pneumococcal meningitis cases, 137 (89.5%) were serotyped; 100 (73.0%) were PCV13-type, including 85 (62.0%) that were serotype 1, a PCV13-targeted serotype. Persons aged ≥5 years accounted for 96.7% (148/153) of cases. Comparing 2015–2016 and 2016–2017 seasons, the proportion of non-serotype 1 PCV13-type cases decreased from 20.0% (9/45) to 4.1% (3/74) (p = 0.008), whereas the proportion that was serotype 1 was stable (71.1% (32/45) vs. 58.1% (43/74); p = 0.16). Estimated adjusted pneumococcal meningitis incidence was 1.8 in children aged <5 years and ranged from 6.8–10.5 in older children and adults. Conclusions: High pneumococcal meningitis incidence with a large proportion of serotype 1 disease in older children and adults suggests infant PCV13 vaccination has not induced herd protection with this schedule in this high-transmission setting. [ABSTRACT FROM AUTHOR]

Influenza virus is an important contributor to acute respiratory illnesses and is estimated to cause up to 650,000 respiratory deaths each year. Ghana recorded influenza viruses as far back as 1918 when the Spanish influenza pandemic led to the death of >100,000 people in a population of 4 million at the time. An outbreak of highly pathogenic avian influenza A(H5N1) among poultry in Ghana in 2007, led to the establishment of virological surveillance for influenza-like illness (ILI) by the Noguchi Memorial Institute for Medical Research (NMIMR). This surveillance system, supported by the U.S. Naval Medical Research Unit-No. 3 (NAMRU-3) and the Ghana Health Service (GHS), monitors circulating influenza strains and activity to better understand the epidemiology of influenza in Ghana. We present here the results of this surveillance system from 2011 to 2019. As part of the Integrated Disease Surveillance and Response (IDSR) system of the GHS under the Ministry of Health (MOH), oropharyngeal and nasopharyngeal swabs were collected from patients who met a modified World Health Organization (WHO) case definition for ILI or severe acute respiratory illness (SARI) through a sentinel surveillance system in the country. Samples were transported to the National Influenza Centre (NIC) at the NMIMR and tested for influenza virus using protocols defined by the United States Centers for Disease Control and Prevention (CDC). Selected isolates were sent to the WHO collaborating centre in the United Kingdom for further antigenic characterization. From 2011 to 2019, the NIC tested a total of 21,747 ILI samples and 3,429 SARI samples. Influenza positivity rates were highest in the 5-14 year old group for both ILI (20.8%) and SARI (23.8%). Compared to females, more males were seen at the health facilities for ILI and SARI symptoms with a statistically significant difference in influenza positive ILI (15% vs 13.2%, p <0.001). In terms of absolute numbers, more cases were seen at the health centres during the wet seasons (April to October) compared to the dry seasons (November to March) in Ghana. This study presents 9 years of surveillance data from outpatient and inpatient setting on influenza activity as well as the influenza A subtypes and B lineages that drive the activity. This presents useful information for influenza vaccine selection and administration. Ghana's unique influenza activity patterns also present a challenge in predicting when an outbreak could occur., Competing Interests: The authors have declared that no competing interests exist., (Copyright: This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.)

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., Competing Interests: The authors declare no competing interests., (Copyright: © 2022 Gibbs et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)