@article{nokey,

title = {Trade-offs among human, animal, and environmental health hinder the uniform progress of global One Health},

author = {Tian, Ya and Zhang, Junze and Li, Zonghan and Wu, Kai and Cao, Min and Lin, Jian and Pradhan, Prajal and Lai, Shengjie and Meng, Jia and Fu, Bojie and Chen, Min and Lin, Hui },

url = {https://doi.org/10.1016/j.isci.2024.111357},

doi = {10.1016/j.isci.2024.111357 },

year  = {2024},

date = {2024-11-22},

journal = {iScience},

volume = {27},

issue = {12},

abstract = {The One Health (OH) approach, integrating aspects of human, animal, and environmental health, still lacks robustly quantified insights into its complex relationships. To fill this knowledge gap, we devised a comprehensive assessment scheme for OH to assess its progress, synergies, trade-offs, and priority targets. From 2000 to 2020, we find evidence for global progress toward OH, albeit uneven, with its average score rising from 61.6 to 65.5, driven primarily by better human health although environmental health lags. Despite synergies prevalent within and between the three health dimensions, over half of the world’s countries, mainly low-income ones, still incur substantial trade-offs impeding OH’s advancement, especially between animal and environmental health. Our in-depth analysis of synergy and trade-off networks reveals that maternal, newborn, and child health are critical synergistic targets, whereas biodiversity and land resources dominate trade-offs. We provide key information for the synergetic and uniform development of global OH and policymaking.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Geospatial Variation in Vaccination Coverage and Zero-Dose Prevalence at the District, Ward and Health Facility Levels Before and After a Measles Vaccination Campaign in Nigeria},

author = {C. Edson Utazi and Iyanuloluwa D. Olowe and H. M. Theophilus Chan and Winfred Dotse-Gborgbortsi and John Wagai and Jamila A. Umar and Sulaiman Etamesor and Brian Atuhaire and Biyi Fafunmi and Jessica Crawford and Adeyemi Adeniran and Andrew J. Tatem},

url = {https://doi.org/10.3390/vaccines12121299},

year  = {2024},

date = {2024-11-21},

journal = {Vaccines},

volume = {12},

number = {1299},

abstract = {Many measles endemic countries with suboptimal coverage levels still rely on vaccination campaigns to fill immunity gaps and boost control efforts. Depending on local epidemiological patterns, national or targeted campaigns are implemented, following which post-campaign coverage surveys (PCCSs) are conducted to evaluate their performance, particularly in terms of reaching previously unvaccinated children. Due to limited resources, PCCS surveys are designed to be representative at coarse spatial scales, often masking important heterogeneities in coverage that could enhance the identification of areas of poor performance for follow-up via routine immunization strategies. Here, we undertake geospatial analyses of the 2021 measles PCCS in Nigeria to map indicators of coverage measuring the individual and combined performance of the campaign and routine immunization (RI) at 1 × 1 km resolution and the ward and district levels in 13 states. Using additional geospatial datasets, we also produced estimates of numbers of unvaccinated children during the campaign and numbers of measles-containing vaccine (MCV) zero-dose children before and after the campaign at these levels and within health facility catchment areas. Our study revealed that although the campaign reduced the numbers of MCV zero-dose children in all the districts, areas of suboptimal campaign and RI performance with considerable numbers of zero-dose children remained. Our analyses further identified wards and health facility catchment areas with higher numbers of unvaccinated children within these areas. Our outputs provide a robust evidence base to plan and implement follow-up RI strategies and to guide future campaigns at flexible and operationally relevant spatial scales.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Disaggregating census data for population mapping using a Bayesian Additive Regression Tree model},

author = {Ortis Yankey and Chigozie E. Utazi and Christopher C. Nnanatu and Assane N. Gadiaga and Thomas Abbot and Attila N. Lazar and Andrew J. Tatem},

url = {https://doi.org/10.1016/j.apgeog.2024.103416},

year  = {2024},

date = {2024-09-14},

journal = {Applied Geography},

volume = {174},

abstract = {Population data is crucial for policy decisions, but fine-scale population numbers are often lacking due to the challenge of sharing sensitive data. Different approaches, such as the use of the Random Forest (RF) model, have been used to disaggregate census data from higher administrative units to small area scales. A major limitation of the RF model is its inability to quantify the uncertainties associated with the predicted populations, which can be important for policy decisions. In this study, we applied a Bayesian Additive Regression Tree (BART) model for population disaggregation and compared the result with a RF model using both simulated data and the 2021 census data for Ghana. The BART model consistently outperforms the RF model in out-of-sample predictions for all metrics, such as bias, mean squared error (MSE), and root mean squared error (RMSE). The BART model also addresses the limitations of the RF model by providing uncertainty estimates around the predicted population, which is often lacking with the RF model. Overall, the study demonstrates the superiority of the BART model over the RF model in disaggregating population data and highlights its potential for gridded population estimates.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Identifying counter-urbanisation using Facebook's user count data},

author = {Qianwen Duan and Jessica Steele and Zhifeng Cheng and Eimear Cleary and Nick Ruktanonchai and Hal Voepel and Tim O'Riordan and Andrew J. Tatem and Alessandro Sorichetta and Shengjie Lai and Felix Eigenbrod},

url = {https://doi.org/10.1016/j.habitatint.2024.103113},

doi = {10.1016/j.habitatint.2024.103113},

year  = {2024},

date = {2024-06-04},

journal = {Habitat International},

volume = {150},

abstract = {Identifying the growing widespread phenomenon of counter-urbanisation, where people relocate from urban centres to rural areas, is essential for understanding the social and ecological consequences of the associated changes. However, its nuanced dynamics and complex characteristics pose challenges for quantitative analysis. Here, we used near real-time Facebook user count data for Belgium and Thailand, with missing data imputed, and applied the Seasonal-Trend decomposition using Loess (STL) model to capture subtle urban and rural population dynamics and assess counter-urbanisation. We identified counter-urbanisation in both Belgium and Thailand, evidenced by increases of 1.80% and 2.14% in rural residents (night-time user counts) and decreases of 3.08% and 5.04% in urban centre night-time user counts from March 2020 to May 2022, respectively. However, the counter-urbanisation in Thailand appears to be transitory, with rural users beginning to decline during both day and night as COVID-19 restrictions were lifted. By contrast, in Belgium, at the country level, there is as yet no evidence of a return to urban residences, though daytime numbers in rural areas are decreasing and in urban centres are increasing, suggesting an increase in commuting post-pandemic. These variation characteristics observed both between Belgium and Thailand and between day and night, extend the current understanding of counter-urbanisation. The use of novel social media data provides an effective quantitative perspective to comprehend counter-urbanisation in different settings.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Inter-district and Wealth-related Inequalities in Maternal and Child Health Service Coverage and Child Mortality within Addis Ababa City},

author = {Mekonnen, W. and Dechassa, W. and Melesse, D.Y. and Tejedor-Garavito, N. and Nilsen, K. and Getachew, T. and Mulu, S. and Wondrad, N},

url = {https://doi.org/10.1007/s11524-024-00836-0},

doi = {doi.org/10.1007/s11524-024-00836-0},

year  = {2024},

date = {2024-03-27},

journal = {Journal of Urban Health},

abstract = {In sub-Saharan Africa, urban areas generally have better access to and use of maternal, newborn, and child health (MNCH) services than rural areas, but previous research indicates that there are significant intra-urban disparities. This study aims to investigate temporal trends and geographic differences in maternal, newborn, and child health service utilization between Addis Ababa’s poorest and richest districts and households. A World Bank district-based poverty index was used to classify districts into the top 60% (non-poor) and bottom 40% (poor), and wealth index data from the Ethiopian Demographic and Health Survey (EDHS) was used to classify households into the top 60% (non-poor) and bottom 40% (poor). Essential maternal, newborn, and child health service coverage was estimated from routine health facility data for 2019–2021, and five rounds of the EDHS (2000–2019) were used to estimate child mortality. The results showed that service coverage was substantially higher in the top 60% than in the bottom 40% of districts. Coverage of four antenatal care visits, skill birth attendance, and postnatal care all exceeded 90% in the non-poor districts but only ranged from 54 to 67% in the poor districts. Inter-district inequalities were less pronounced for childhood vaccinations, with over 90% coverage levels across all districts. Inter-district inequalities in mortality rates were considerable. The neonatal mortality rate was nearly twice as high in the bottom 40% of households' as in the top 60% of households. Similarly, the under-5 mortality rate was three times higher in the bottom 40% compared to the top 60% of households. The substantial inequalities in MNCH service utilization and child mortality in Addis Ababa highlight the need for greater focus on the city’s women and children living in the poorest households and districts in maternal, newborn, and child health programs.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Building footprint data for countries in Africa: To what extent are existing data products comparable?},

author = {Heather R. Chamberlain and Edith Darin and Wole Ademola Adewole and Warren C. Jochem and Attila N. Lazar and Andrew J. Tatem},

url = {https://doi.org/10.1016/j.compenvurbsys.2024.102104},

doi = {10.1016/j.compenvurbsys.2024.102104},

year  = {2024},

date = {2024-03-22},

journal = {Computers, Environment and Urban Systems},

volume = {110},

abstract = {Growth and developments in computing power, machine-learning algorithms and satellite imagery spatiotemporal resolution have led to rapid developments in automated feature-extraction. These methods have been applied to create geospatial datasets of features such as roads, trees and building footprints, at a range of spatial scales, with national and multi-country datasets now available as open data from multiple sources. Building footprint data is particularly useful in a range of applications including mapping population distributions, planning resource distribution campaigns and in humanitarian response. In settings with well-developed geospatial data systems, such datasets may complement existing authoritative sources, but in data-scarce settings, they may be the only source of data. However, knowledge on the degree to which building footprint data products are comparable and can be used interchangeably, and the impact of selecting a particular dataset on subsequent analyses remains limited. For all countries in Africa, we review the available multi-country building footprint data products and analyse their similarities and differences in terms of building area and count metrics. We explore the variation between building footprint data products across a range of spatial scales, including sub-national administrative units and different settlement types. Our results show that the available building footprint data products are not interchangeable. There are clear differences in counts and total area of building footprints between the assessed data products, as well as considerable spatial heterogeneity in building footprint coverage and completeness.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Demographic, Clinical Profile of Rheumatoid Arthritis Patients and Their Association with Disease Severity in Ghana},

author = {Tonnies Abeku Buckman and Samuel Asamoah Sakyi and Kwame Yeboah-Mensah and Maxwell Hubert Antwi and Isaac Darban and Lawrence Owusu-Brenya and Joseph Yorke and Andy Opoku Boateng and Ebenezer Senu and Albert Dompreh and Akwasi Minnah Addei and Richard Boateng and Ortis Yankey and Samuel Tandoh},

url = {https://doi.org/10.1155/2024/6639079},

doi = {10.1155/2024/6639079},

year  = {2024},

date = {2024-01-12},

journal = {International Journal of Rheumatology},

abstract = {Rheumatoid arthritis (RA) is one of the frequent chronic, systemic, inflammatory autoimmune disorders with an estimated global prevalence of 1%. RA leads to joint destruction and disability if left untreated. Ghana has seen very few studies on RA, and little is known about the disease’s severity and related variables. This study sought to characterize the clinical presentation and determine disease severity and associated risk factors with disease severity among RA patients in a tertiary hospital in Ghana. 



This cross-sectional study was conducted between September 2020 and August 2021. This study included 56 consecutively consenting RA patients from the Komfo Anokye Teaching Hospital orthopaedic unit. Diagnosis of RA was based on the updated American College of Rheumatology/European League Against Rheumatism (ACR/EULAR) 2022 rheumatoid arthritis classification criteria by a rheumatologist. A study questionnaire was used to gather participant demographics and clinical features, and results from the laboratory were taken from the patients’ charts and medical records. The patients’ disease severity was evaluated based on the rheumatoid arthritis disease activity score, which is based on a 28-joint count (DAS28), and their functioning was evaluated using the modified health assessment questionnaire.



The participants’ mean age was  years. Out of the total participants, 46 were females, and 10 were males (female-to-male ratio 4.6 : 1). Moreover, 37.50% had arthritis of the hand; 5.30% had severe disease, and 94.60% were not severe. A majority (76.80%) were on methotrexate medication. The most frequently involved joints were the knee (42.90%), wrist (32.10%), and elbow (12.50%). There was no statistically significant association with disease severity and a functional status score of >0.5 (cOR: 10.60, 95% CI (0.52-217.30); ). In addition, marital status (), disease duration (), family complaints (), and ESR () were significantly associated with disease severity. 



RA is predominant among elder populations and females. Disease duration, family complaints, and ESR are associated with disease severity. The findings of this study call for interventions towards ensuring early diagnosis of RA among high-risk populations to enhance good management practices.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{zhang2024optimizing,

title = {Optimizing the detection of emerging infections using mobility-based spatial sampling},

author = {Die Zhang and Yong Ge and Jianghao Wang and Haiyan Liu and Wen-Bin Zhang and Xilin Wu and Gerard BM Heuvelink and Chaoyang Wu and Juan Yang and Nick W Ruktanonchai and others},

url = {https://doi.org/10.1016/j.jag.2024.103949},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {International Journal of Applied Earth Observation and Geoinformation},

volume = {131},

pages = {103949},

publisher = {Elsevier},

abstract = {Timely and precise detection of emerging infections is imperative for effective outbreak management and disease control. Human mobility significantly influences the spatial transmission dynamics of infectious diseases. Spatial sampling, integrating the spatial structure of the target, holds promise as an approach for testing allocation in detecting infections, and leveraging information on individuals’ movement and contact behavior can enhance targeting precision. This study introduces a spatial sampling framework informed by spatiotemporal analysis of human mobility data, aiming to optimize the allocation of testing resources for detecting emerging infections. Mobility patterns, derived from clustering point-of-interest and travel data, are integrated into four spatial sampling approaches at the community level. We evaluate the proposed mobility-based spatial sampling by analyzing both actual and simulated outbreaks, considering scenarios of transmissibility, intervention timing, and population density in cities. Results indicate that leveraging inter-community movement data and initial case locations, the proposed Case Flow Intensity (CFI) and Case Transmission Intensity (CTI)-informed spatial sampling enhances community-level testing efficiency by reducing the number of individuals screened while maintaining a high accuracy rate in infection identification. Furthermore, the prompt application of CFI and CTI within cities is crucial for effective detection, especially in highly contagious infections within densely populated areas. With the widespread use of human mobility data for infectious disease responses, the proposed theoretical framework extends spatiotemporal data analysis of mobility patterns into spatial sampling, providing a cost-effective solution to optimize testing resource deployment for containing emerging infectious diseases.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{li2024chronic,

title = {Chronic disease and infection in China: lessons from the covid-19 pandemic},

author = {Yanming Li and Xunliang Tong and Mingyue Jiang and Zeni Wu and Yuping Duan and Li Zhang and Shengjie Lai and Weizhong Yang and Luzhao Feng},

url = {https://doi.org/10.1136/bmj.q2000},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {bmj},

volume = {387},

publisher = {British Medical Journal Publishing Group},

abstract = {The harms from covid-19 during the pandemic extended beyond the disease itself. Older people with a chronic or non-communicable disease were among the most vulnerable to SARS-CoV-2 and at the highest risk of death. The interplay between chronic disease and infection can exacerbate each condition, highlighting the importance of preventing infection in people with chronic disease and increasing the challenges of caring for patients with comorbidity. For instance, during the omicron outbreak in Hong Kong excess deaths occurred not only from covid-19 infections but from chronic diseases, including cerebrovascular disease and kidney disease. This may be because the healthcare system was heavily overwhelmed, leading to low quality healthcare services and longer waits for some interventions for non-covid illness. Moreover, patients with chronic diseases may face increased risks of complications and severe outcomes after covid-19 infection.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Geospatial Analyses of Recent Household Surveys to Assess Changes in the Distribution of Zero-Dose Children and Their Associated Factors before and during the COVID-19 Pandemic in Nigeria},

author = {Justice Moses K. Aheto and Iyanuloluwa Deborah Olowe and Ho Man Theophilus Chan and Adachi Ekeh and Boubacar Dieng and Biyi Fafunmi and Hamidreza Setayesh and Brian Atuhaire and Jessica Crawford and Andrew J. Tatem and Chigozie Edson Utazi},

url = {https://doi.org/10.3390/vaccines11121830},

doi = {10.3390/vaccines11121830 },

year  = {2023},

date = {2023-12-08},

journal = {Vaccines},

volume = {11},

issue = {12},

abstract = {The persistence of geographic inequities in vaccination coverage often evidences the presence of zero-dose and missed communities and their vulnerabilities to vaccine-preventable diseases. These inequities were exacerbated in many places during the coronavirus disease 2019 (COVID-19) pandemic, due to severe disruptions to vaccination services. Understanding changes in zero-dose prevalence and its associated risk factors in the context of the COVID-19 pandemic is, therefore, critical to designing effective strategies to reach vulnerable populations. Using data from nationally representative household surveys conducted before the COVID-19 pandemic, in 2018, and during the pandemic, in 2021, in Nigeria, we fitted Bayesian geostatistical models to map the distribution of three vaccination coverage indicators: receipt of the first dose of diphtheria-tetanus-pertussis-containing vaccine (DTP1), the first dose of measles-containing vaccine (MCV1), and any of the four basic vaccines (bacilli Calmette-Guerin (BCG), oral polio vaccine (OPV0), DTP1, and MCV1), and the corresponding zero-dose estimates independently at a 1 × 1 km resolution and the district level during both time periods. We also explored changes in the factors associated with non-vaccination at the national and regional levels using multilevel logistic regression models. Our results revealed no increases in zero-dose prevalence due to the pandemic at the national level, although considerable increases were observed in a few districts. We found substantial subnational heterogeneities in vaccination coverage and zero-dose prevalence both before and during the pandemic, showing broadly similar patterns in both time periods. Areas with relatively higher zero-dose prevalence occurred mostly in the north and a few places in the south in both time periods. We also found consistent areas of low coverage and high zero-dose prevalence using all three zero-dose indicators, revealing the areas in greatest need. At the national level, risk factors related to socioeconomic/demographic status (e.g., maternal education), maternal access to and utilization of health services, and remoteness were strongly associated with the odds of being zero dose in both time periods, while those related to communication were mostly relevant before the pandemic. These associations were also supported at the regional level, but we additionally identified risk factors specific to zero-dose children in each region; for example, communication and cross-border migration in the northwest. Our findings can help guide tailored strategies to reduce zero-dose prevalence and boost coverage levels in Nigeria.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Spatiotemporal variations of “triple-demic” outbreaks of respiratory infections in the United States in the post-COVID-19 era},

author = {Wei Luo and Qianhuang Liu and Yuxuan Zhou and Yiding Ran and Zhaoyin Liu and Weitao Hou and Sen Pei and Shengjie Lai},

url = {https://doi.org/10.1186/s12889-023-17406-9},

doi = {10.1186/s12889-023-17406-9},

year  = {2023},

date = {2023-12-07},

journal = {BMC Public Health},

volume = {23},

number = {2452},

abstract = {The US confronted a “triple-demic” of influenza, respiratory syncytial virus (RSV), and COVID-19 in the winter of 2022, leading to increased respiratory infections and a higher demand for medical supplies. It is urgent to analyze these epidemics and their spatial-temporal co-occurrence, identifying hotspots and informing public health strategies.



We employed retrospective and prospective space-time scan statistics to assess the situations of COVID-19, influenza, and RSV in 51 US states from October 2021 to February 2022, and from October 2022 to February 2023, respectively. This enabled monitoring of spatiotemporal variations for each epidemic individually and collectively.



Compared to winter 2021, COVID-19 cases decreased while influenza and RSV infections significantly increased in winter 2022. We found a high-risk cluster of influenza and COVID-19 (not all three) in winter 2021. In late November 2022, a large high-risk cluster of triple-demic emerged in the central US. The number of states at high risk for multiple epidemics increased from 15 in October 2022 to 21 in January 2023.



Our study offers a novel spatiotemporal approach that combines both univariate and multivariate surveillance, as well as retrospective and prospective analyses. This approach offers a more comprehensive and timely understanding of how the co-occurrence of COVID-19, influenza, and RSV impacts various regions within the United States. Our findings assist in tailor-made strategies to mitigate the effects of these respiratory infections.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Prior water availability modifies the effect of heavy rainfall on dengue transmission: a time series analysis of passive surveillance data from southern China},

author = {Qu Cheng and Qinlong Jing and Philip A. Collender and Jennifer R. Head and Qi Li and Hailan Yu and Zhichao Li and Yang Ju and Tianmu Chen and Peng Wang and Eimear Cleary and Shengjie Lai},

url = {https://doi.org/10.3389/fpubh.2023.1287678},

doi = {10.3389/fpubh.2023.1287678},

year  = {2023},

date = {2023-12-01},

journal = {Frontiers in Public Health},

abstract = {Given the rapid geographic spread of dengue and the growing frequency and intensity of heavy rainfall events, it is imperative to understand the relationship between these phenomena in order to propose effective interventions. However, studies exploring the association between heavy rainfall and dengue infection risk have reached conflicting conclusions, potentially due to the neglect of prior water availability in mosquito breeding sites as an effect modifier.



In this study, we addressed this research gap by considering the impact of prior water availability for the first time. We measured prior water availability as the cumulative precipitation over the preceding 8 weeks and utilized a distributed lag non-linear model stratified by the level of prior water availability to examine the association between dengue infection risk and heavy rainfall in Guangzhou, a dengue transmission hotspot in southern China.



Our findings suggest that the effects of heavy rainfall are likely to be modified by prior water availability. A 24–55 day lagged impact of heavy rainfall was associated with an increase in dengue risk when prior water availability was low, with the greatest incidence rate ratio (IRR) of 1.37 [95% credible interval (CI): 1.02–1.83] occurring at a lag of 27 days. In contrast, a heavy rainfall lag of 7–121 days decreased dengue risk when prior water availability was high, with the lowest IRR of 0.59 (95% CI: 0.43–0.79), occurring at a lag of 45 days.



These findings may help to reconcile the inconsistent conclusions reached by previous studies and improve our understanding of the complex relationship between heavy rainfall and dengue infection risk.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Impact of Urban Slum Residence on Coverage of Maternal, Neonatal, and Child Health Service Indicators in the Greater Accra Region of Ghana: an Ecological Time-Series Analysis, 2018-2021},

author = {Duah Dwomoh and Samuel Iddi and Seth Kwaku Afagbedzi and Natalia Tejedor-Garavito and Winfred Dotse-Gborgbortsi and Jim Wright and Andrew J Tatem and Kristine Nilsen },

url = {https://doi.org/10.1007/s11524-023-00812-0

},

doi = {10.1007/s11524-023-00812-0},

year  = {2023},

date = {2023-11-16},

journal = {Journal of Urban Health},

abstract = {Among other focus areas, the global Sustainable Development Goals (SDGs) 3 and 11 seek to advance progress toward universal coverage of maternal, neonatal, and child health (MNCH) services and access to safe and affordable housing and basic services by 2030. Governments and development agencies have historically neglected the health and well-being associated with living in urban slums across major capital cities in sub-Saharan Africa since health policies and programs have tended to focus on people living in rural communities. This study assessed the trends and compared inequities in MNCH service utilization between slum and non-slum districts in the Greater Accra region of Ghana. It analyzed information from 29 districts using monthly time-series Health Management Information System (HMIS) data on MNCH service utilization between January 2018 and December 2021. Multivariable quantile regression models with robust standard errors were used to quantify the impact of urban slum residence on MNCH service utilization. We assessed the inequality of MNCH coverage indicators between slum and non-slum districts using the Gini index with bootstrapped standard errors and the generalized Lorenz curve. The results indicate that rates of vaccination coverage and antenatal care (ANC) attendance have declined significantly in slum districts compared to those in non-slum districts. However, skilled birth delivery and postnatal care (PNC) were found to be higher in urban slum areas compared to those in non-urban slum areas. To help achieve the SDGs’ targets, it is important for the government of Ghana and other relevant stakeholders to prioritize the implementation of effective policies, programs, and interventions that will improve access to and utilization of ANC and immunization services among urban slum dwellers.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Imported and indigenous Plasmodium Vivax and Plasmodium Falciparum malaria in the Hubei Province of China, 2005–2019},

url = {https://doi.org/10.1186/s12936-023-04752-7

},

doi = {10.1186/s12936-023-04752-7},

year  = {2023},

date = {2023-11-06},

journal = {Malaria Journal},

number = {334},

issue = {22},

abstract = {The Hubei Province in China reported its last indigenous malaria case in September 2012, but imported malaria cases, particularly those related to Plasmodium vivax and Plasmodium falciparum, threaten Hubei’s malaria-free status. This study investigated the epidemiological changes in P. vivax and P. falciparum malaria in this province to provide scientific evidence for preventing malaria resurgence.



The prevalence, demographic characteristics, seasonal features, and geographical distribution of malaria were assessed using surveillance data and were compared across three stages: control stage (2005–2009) and elimination stages I (2010–2014) and II (2015–2019).



In 2005–2019, 8483 malaria cases were reported, including 5599 indigenous P. vivax cases, 275 imported P. vivax cases, 866 imported P. falciparum cases, and 1743 other cases. Imported P. falciparum cases accounted for 0.07% of all cases reported in 2005, but increased to 78.81% in 2019. Most imported P. vivax and P. falciparum malaria occurred among males, aged 21–60 years, during elimination stages I and II. The number of regions affected by imported P. falciparum and P. vivax increased markedly in Hubei from the control stage to elimination stage II. Overall, 1125 imported P. vivax and P. falciparum cases were detected from 47 other nations. Eight imported cases were detected from other provinces in China. From the control stage to elimination stage II, the number of cases of malaria imported from African countries increased, and that of cases imported from Southeast Asian countries decreased.



Although Hubei has achieved malaria elimination, it faces challenges in maintaining this status. Hence, imported malaria surveillance need to be strengthened to reduce the risk of malaria re-introduction.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Exploring the Relationship between Temporal Fluctuations in Satellite Nightlight Imagery and Human Mobility across Africa},

author = {Grant Rogers and Patrycja Koper and Cori Ruktanonchai and and Nick Ruktanonchai and Edson Utazi and Dorothea Woods and Alexander Cunningham and Andrew J. Tatem and Jessica Steele and Shengjie Lai and Alessandro Sorichetta},

url = {https://doi.org/10.3390/rs15174252},

doi = {10.3390/rs15174252},

year  = {2023},

date = {2023-09-30},

journal = {Remote Sensing},

volume = {15},

number = {4252;},

issue = {17},

abstract = {Mobile phone data have been increasingly used over the past decade or more as a pretty reliable indicator of human mobility to measure population movements and the associated changes in terms of population presence and density at multiple spatial and temporal scales. However, given the fact mobile phone data are not available everywhere and are generally difficult to access and share, mostly because of commercial restrictions and privacy concerns, more readily available data with global coverage, such as night-time light (NTL) imagery, have been alternatively used as a proxy for population density changes due to population movements. This study further explores the potential to use NTL brightness as a short-term mobility metric by analysing the relationship between NTL and smartphone-based Google Aggregated Mobility Research Dataset (GAMRD) data across twelve African countries over two periods: 2018–2019 and 2020. The data were stratified by a measure of the degree of urbanisation, whereby the administrative units of each country were assigned to one of eight classes ranging from low-density rural to high-density urban. Results from the correlation analysis, between the NTL Sum of Lights (SoL) radiance values and three different GAMRD-based flow metrics calculated at the administrative unit level, showed significant differences in NTL-GAMRD correlation values across the eight rural/urban classes. The highest correlations were typically found in predominantly rural areas, suggesting that the use of NTL data as a mobility metric may be less reliable in predominantly urban settings. This is likely due to the brightness saturation and higher brightness stability within the latter, showing less of an effect than in rural or peri-urban areas of changes in brightness due to people leaving or arriving. Human mobility in 2020 (during COVID-19-related restrictions) was observed to be significantly different than in 2018–2019, resulting in a reduced NTL-GAMRD correlation strength, especially in urban settings, most probably because of the monthly NTL SoL radiance values remaining relatively similar in 2018–2019 and 2020 and the human mobility, especially in urban settings, significantly decreasing in 2020 with respect to the previous considered period. The use of NTL data on its own to assess monthly mobility and the associated fluctuations in population density was therefore shown to be promising in rural and peri-urban areas but problematic in urban settings.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Validating indicators for monitoring availability and geographic distribution of emergency obstetric and newborn care (EmoNC) facilities: A study triangulating health system, facility, and geospatial data},

author = {Jewel Gausman and Verónica Pingray and Richard Adanu and Delia A. B. Bandohand Mabel Berrueta and Jeff Blossom and Suchandrima Chakraborty and Winfred Dotse-Gborgbortsi and Ernest Kenu and Nizamuddin Khan and Ana Langer and Carolina Nigri and Magdalene A. Odikro and Sowmya Ramesh and Niranjan Saggurti and Paula Vázquez and Caitlin R. Williams and R. Rima Jolivet},

url = {https://doi.org/10.1371/journal.pone.0287904

},

doi = {10.1371/journal.pone.0287904},

year  = {2023},

date = {2023-09-14},

journal = {PLOS one},

abstract = {Availability of emergency obstetric and newborn care (EmONC) is a strong supply side measure of essential health system capacity that is closely and causally linked to maternal mortality reduction and fundamentally to achieving universal health coverage. The World Health Organization’s indicator “Availability of EmONC facilities” was prioritized as a core indicator to prevent maternal death. The indicator focuses on whether there are sufficient emergency care facilities to meet the population need, but not all facilities designated as providing EmONC function as such. This study seeks to validate “Availability of EmONC” by comparing the value of the indicator after accounting for key aspects of facility functionality and an alternative measure of geographic distribution. This study takes place in four subnational geographic areas in Argentina, Ghana, and India using a census of all birthing facilities. Performance of EmONC in the 90 days prior to data collection was assessed by examining facility records. Data were collected on facility operating hours, staffing, and availability of essential medications. Population estimates were generated using ArcGIS software using WorldPop to estimate the total population, and the number of women of reproductive age (WRA), pregnancies and births in the study areas. In addition, we estimated the population within two-hours travel time of an EmONC facility by incorporating data on terrain from Open Street Map. Using these data sources, we calculated and compared the value of the indicator after incorporating data on facility performance and functionality while varying the reference population used. Further, we compared its value to the proportion of the population within two-hours travel time of an EmONC facility. Included in our study were 34 birthing facilities in Argentina, 51 in Ghana, and 282 in India. Facility performance of basic EmONC (BEmONC) and comprehensive EmONC (CEmONC) signal functions varied considerably. One facility (4.8%) in Ghana and no facility in India designated as BEmONC had performed all seven BEmONC signal functions. In Argentina, three (8.8%) CEmONC-designated facilities performed all nine CEmONC signal functions, all located in Buenos Aires Region V. Four CEmONC-designated facilities in Ghana (57.1%) and the three CEmONC-designated facilities in India (23.1%) evidenced full CEmONC performance. No sub-national study area in Argentina or India reached the target of 5 BEmONC-level facilities per 20,000 births after incorporating facility functionality yet 100% did in Argentina and 50% did in India when considering only facility designation. Demographic differences also accounted for important variation in the indicator’s value. In Ghana, the total population in Tolon within 2 hours travel time of a designated EmONC facility was estimated at 99.6%; however, only 91.1% of women of reproductive age were within 2 hours travel time. Comparing the value of the indicator when calculated using different definitions reveals important inconsistencies, resulting in conflicting information about whether the threshold for sufficient coverage is met. This raises important questions related to the indicator’s validity. To provide a valid measure of effective coverage of EmONC, the construct for measurement should extend beyond the most narrow definition of availability and account for functionality and geographic accessibility.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {A high-resolution daily global dataset of statistically downscaled CMIP6 models for climate impact analyses},

author = {Solomon Gebrechorkos and Julian Leyland and Louise Slater and Michel Wortmann and Philip J. Ashworth and Georgina L. Bennett and Richard Boothroyd and Hannah Cloke and Pauline Delorme and Helen Griffith and Richard Hardy and Laurence Hawker and Stuart McLelland and Jeffrey Neal and Andrew Nicholas and Andrew J. Tatem and Ellie Vahidi and Daniel R. Parsons and Stephen E. Darby},

url = {https://doi.org/10.1038/s41597-023-02528-x

},

doi = {10.1038/s41597-023-02528-x},

year  = {2023},

date = {2023-09-11},

urldate = {2023-09-11},

journal = {Scientific Data},

number = {611},

issue = {10},

abstract = {A large number of historical simulations and future climate projections are available from Global Climate Models, but these are typically of coarse resolution, which limits their effectiveness for assessing local scale changes in climate and attendant impacts. Here, we use a novel statistical downscaling model capable of replicating extreme events, the Bias Correction Constructed Analogues with Quantile mapping reordering (BCCAQ), to downscale daily precipitation, air-temperature, maximum and minimum temperature, wind speed, air pressure, and relative humidity from 18 GCMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6). BCCAQ is calibrated using high-resolution reference datasets and showed a good performance in removing bias from GCMs and reproducing extreme events. The globally downscaled data are available at the Centre for Environmental Data Analysis (https://doi.org/10.5285/c107618f1db34801bb88a1e927b82317) for the historical (1981–2014) and future (2015–2100) periods at 0.25° resolution and at daily time step across three Shared Socioeconomic Pathways (SSP2-4.5, SSP5-3.4-OS and SSP5-8.5). This new climate dataset will be useful for assessing future changes and variability in climate and for driving high-resolution impact assessment models.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Mapping the timeliness of routine childhood vaccination in The Gambia: A spatial modelling study},

editor = {Oghenebrume Wariri and Chigozie Edson Utazi and Uduak Okomo and C. Jessica E. Metcalf and Malick Sogur and Sidat Fofana and Kris A. Murray and Chris Grundy and Beate Kampmann},

url = {https://doi.org/10.1016/j.vaccine.2023.08.004},

doi = {10.1016/j.vaccine.2023.08.004},

year  = {2023},

date = {2023-09-09},

journal = {Vaccine},

volume = {41},

issue = {39},

abstract = {Timeliness of routine vaccination shapes childhood infection risk and thus is an important public health metric. Estimates of indicators of the timeliness of vaccination are usually produced at the national or regional level, which may conceal epidemiologically relevant local heterogeneities and make it difficult to identify pockets of vulnerabilities that could benefit from targeted interventions. Here, we demonstrate the utility of geospatial modelling techniques in generating high-resolution maps of the prevalence of delayed childhood vaccination in The Gambia. To guide local immunisation policy and prioritize key interventions, we also identified the districts with a combination of high estimated prevalence and a significant population of affected infants.



We used the birth dose of the hepatitis-B vaccine (HepB0), third-dose of the pentavalent vaccine (PENTA3), and the first dose of measles-containing vaccine (MCV1) as examples to map delayed vaccination nationally at a resolution of 1 × 1-km2 pixel. We utilized cluster-level childhood vaccination data from The Gambia 2019–20 Demographic and Health Survey. We adopted a fully Bayesian geostatistical model incorporating publicly available geospatial covariates to aid predictive accuracy. The model was implemented using the integrated nested Laplace approximation—stochastic partial differential equation (INLA-SPDE) approach.



We found significant subnational heterogeneity in delayed HepB0, PENTA3 and MCV1 vaccinations. Specific districts in the central and eastern regions of The Gambia consistently exhibited the highest prevalence of delayed vaccination, while the coastal districts showed a lower prevalence for all three vaccines. We also found that districts in the eastern, central, as well as in coastal parts of The Gambia had a combination of high estimated prevalence of delayed HepB0, PENTA3 and MCV1 and a significant population of affected infants.



Our approach provides decision-makers with a valuable tool to better understand local patterns of untimely childhood vaccination and identify districts where strengthening vaccine delivery systems could have the greatest impact.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {A zero-dose vulnerability index for equity assessment and spatial prioritization in low- and middle-income countries},

author = {C.E. Utazi and H.M.T. Chan and I. Olowe and A. Wigley and N. Tejedor-Garavito and A. Cunningham and M. Bondarenko and J. Lorin and D. Boyda and D. Hogan and A.J. Tatem},

url = {https://doi.org/10.1016/j.spasta.2023.100772

},

doi = {10.1016/j.spasta.2023.100772},

year  = {2023},

date = {2023-09-05},

journal = {Spatial Statistics},

number = {100772},

abstract = {Many low- and middle-income countries (LMICs) continue to experience substantial inequities in vaccination coverage despite recent efforts to reach missed communities and reduce zero-dose prevalence. Geographic inequities in vaccination coverage are often characterized by a multiplicity of risk factors which should be operationalized through data integration to inform more effective and equitable vaccination policies and programmes. Here, we explore approaches for integrating information from multiple risk factors to create a zero-dose vulnerability index to improve the identification and prioritization of vulnerable communities and understanding of inequities in vaccination coverage. We assembled geolocated data on vaccination coverage and associated risk factors in six LMICs, focusing on the coverage of DTP1, DTP3 and MCV1 vaccines as indicators of zero dose and under-vaccination. Using geospatial modelling techniques built on a suite of geospatial covariate information, we produced 1 × 1 km and district level maps of the previously unmapped risk factors and vaccination coverage. We then integrated data from the maps of the risk factors using different approaches to construct a zero-dose vulnerability index to classify districts within the countries into different vulnerability groups, ranging from the least vulnerable (1) to the most vulnerable (5) areas. Through integration with population data, we estimated numbers of children aged under 1 living within the different vulnerability classes. Our results show substantial variation in the spatial distribution of the index, revealing the most vulnerable areas despite little variation in coverage in some cases. We found that the most distinguishing characteristics of the most vulnerable areas cut across the different subdomains (health, socioeconomic, demographic and geographic) of the risk factors included in our study. We also demonstrated that the index can be robustly estimated with fewer risk factors and without linkage to information on vaccination coverage. The index constitutes a practical and effective tool to guide targeted vaccination strategies in LMICs.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Effects of public-health measures for zeroing out different SARS-CoV-2 variants},

author = {Yong Ge and Xilin Wu and Wenbin Zhang and Xiaoli Wang and Die Zhang and Jianghao Wang and Haiyan Liu and Zhoupeng Ren and Nick W. Ruktanonchai and Corrine W. Ruktanonchai and Eimear Cleary and Yongcheng Yao and Amy Wesolowski and Derek A. T. Cummings and Zhongjie Li and Andrew J. Tatem and Shengjie La},

url = {https://doi.org/10.1038/s41467-023-40940-4

},

doi = {10.1038/s41467-023-40940-4},

year  = {2023},

date = {2023-08-29},

urldate = {2023-08-29},

journal = {Nature Communications},

volume = {14},

number = {5270},

abstract = {Targeted public health interventions for an emerging epidemic are essential for preventing pandemics. During 2020-2022, China invested significant efforts in strict zero-COVID measures to contain outbreaks of varying scales caused by different SARS-CoV-2 variants. Based on a multi-year empirical dataset containing 131 outbreaks observed in China from April 2020 to May 2022 and simulated scenarios, we ranked the relative intervention effectiveness by their reduction in instantaneous reproduction number. We found that, overall, social distancing measures (38% reduction, 95% prediction interval 31-45%), face masks (30%, 17-42%) and close contact tracing (28%, 24-31%) were most effective. Contact tracing was crucial in containing outbreaks during the initial phases, while social distancing measures became increasingly prominent as the spread persisted. In addition, infections with higher transmissibility and a shorter latent period posed more challenges for these measures. Our findings provide quantitative evidence on the effects of public-health measures for zeroing out emerging contagions in different contexts.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}