Optimizing the detection of emerging infections using mobility-based spatial sampling.

Background: Timely and precise detection of emerging infections is crucial for effective outbreak management and disease control. Human mobility significantly influences infection risks and transmission dynamics, and spatial sampling is a valuable tool for pinpointing potential infections in specific areas. This study explored spatial sampling methods, informed by various mobility patterns, to optimize the allocation of testing resources for detecting emerging infections. Methods: Mobility patterns, derived from clustering point-of-interest data and travel data, were integrated into four spatial sampling approaches to detect emerging infections at the community level. To evaluate the effectiveness of the proposed mobility-based spatial sampling, we conducted analyses using actual and simulated outbreaks under different scenarios of transmissibility, intervention timing, and population density in cities. Results: By leveraging inter-community movement data and initial case locations, the proposed case flow intensity (CFI) and case transmission intensity (CTI)-informed sampling approaches could considerably reduce the number of tests required for both actual and simulated outbreaks. Nonetheless, the prompt use of CFI and CTI within communities is imperative for effective detection, particularly for highly contagious infections in densely populated areas. Conclusions: The mobility-based spatial sampling approach can substantially improve the efficiency of community-level testing for detecting emerging infections. It achieves this by reducing the number of individuals screened while maintaining a high accuracy rate of infection identification. It represents a cost-effective solution to optimize the deployment of testing resources, when necessary, to contain emerging infectious diseases in diverse settings.

Effects of public-health measures for zeroing out different SARS-CoV-2 variants.

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.

Untangling the changing impact of non-pharmaceutical interventions and vaccination on European COVID-19 trajectories.

Non-pharmaceutical interventions (NPIs) and vaccination are two fundamental approaches for mitigating the coronavirus disease 2019 (COVID-19) pandemic. However, the real-world impact of NPIs versus vaccination, or a combination of both, on COVID-19 remains uncertain. To address this, we built a Bayesian inference model to assess the changing effect of NPIs and vaccination on reducing COVID-19 transmission, based on a large-scale dataset including epidemiological parameters, virus variants, vaccines, and climate factors in Europe from August 2020 to October 2021. We found that (1) the combined effect of NPIs and vaccination resulted in a 53% (95% confidence interval: 42-62%) reduction in reproduction number by October 2021, whereas NPIs and vaccination reduced the transmission by 35% and 38%, respectively; (2) compared with vaccination, the change of NPI effect was less sensitive to emerging variants; (3) the relative effect of NPIs declined 12% from May 2021 due to a lower stringency and the introduction of vaccination strategies. Our results demonstrate that NPIs were complementary to vaccination in an effort to reduce COVID-19 transmission, and the relaxation of NPIs might depend on vaccination rates, control targets, and vaccine effectiveness concerning extant and emerging variants.

Impacts of worldwide individual non-pharmaceutical interventions on COVID-19 transmission across waves and space.

Governments worldwide have rapidly deployed non-pharmaceutical interventions (NPIs) to mitigate the COVID-19 pandemic. However, the effect of these individual NPI measures across space and time has yet to be sufficiently assessed, especially with the increase of policy fatigue and the urge for NPI relaxation in the vaccination era. Using the decay ratio in the suppression of COVID-19 infections and multi-source big data, we investigated the changing performance of different NPIs across waves from global and regional levels (in 133 countries) to national and subnational (in the United States of America [USA]) scales before the implementation of mass vaccination. The synergistic effectiveness of all NPIs for reducing COVID-19 infections declined along waves, from 95.4% in the first wave to 56.0% in the third wave recently at the global level and similarly from 83.3% to 58.7% at the USA national level, while it had fluctuating performance across waves on regional and subnational scales. Regardless of geographical scale, gathering restrictions and facial coverings played significant roles in epidemic mitigation before the vaccine rollout. Our findings have important implications for continued tailoring and implementation of NPI strategies, together with vaccination, to mitigate future COVID-19 waves, caused by new variants, and other emerging respiratory infectious diseases.

Global holiday datasets for understanding seasonal human mobility and population dynamics.

Public and school holidays have important impacts on population mobility and dynamics across multiple spatial and temporal scales, subsequently affecting the transmission dynamics of infectious diseases and many socioeconomic activities. However, worldwide data on public and school holidays for understanding their changes across regions and years have not been assembled into a single, open-source and multitemporal dataset. To address this gap, an open access archive of data on public and school holidays in 2010-2019 across the globe at daily, weekly, and monthly timescales was constructed. Airline passenger volumes across 90 countries from 2010 to 2018 were also assembled to illustrate the usage of the holiday data for understanding the changing spatiotemporal patterns of population movements.

Risk of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Transmission Among Air Passengers in China.

BACKGROUND: Modern transportation plays a key role in the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and new variants. However, little is known about the exact transmission risk of the virus on airplanes. METHODS: Using the itinerary and epidemiological data of coronavirus disease 2019 (COVID-19) cases and close contacts on domestic airplanes departing from Wuhan city in China before the lockdown on 23 January 2020, we estimated the upper and lower bounds of overall transmission risk of COVID-19 among travelers. RESULTS: In total, 175 index cases were identified among 5797 passengers on 177 airplanes. The upper and lower attack rates (ARs) of a seat were 0.60% (34/5622, 95% confidence interval [CI] .43-.84%) and 0.33% (18/5400, 95% CI .21-.53%), respectively. In the upper- and lower-bound risk estimates, each index case infected 0.19 (SD 0.45) and 0.10 (SD 0.32) cases, respectively. The seats immediately adjacent to the index cases had an AR of 9.2% (95% CI 5.7-14.4%), with a relative risk 27.8 (95% CI 14.4-53.7) compared to other seats in the upper limit estimation. The middle seat had the highest AR (0.7%, 95% CI .4%-1.2%). The upper-bound AR increased from 0.7% (95% CI 0.5%-1.0%) to 1.2% (95% CI .4-3.3%) when the co-travel time increased from 2.0 hours to 3.3 hours. CONCLUSIONS: The ARs among travelers varied by seat distance from the index case and joint travel time, but the variation was not significant between the types of aircraft. The overall risk of SARS-CoV-2 transmission during domestic travel on planes was relatively low. These findings can improve our understanding of COVID-19 spread during travel and inform response efforts in the pandemic.

Practical geospatial and sociodemographic predictors of human mobility.

Understanding seasonal human mobility at subnational scales has important implications across sciences, from urban planning efforts to disease modelling and control. Assessing how, when, and where populations move over the course of the year, however, requires spatially and temporally resolved datasets spanning large periods of time, which can be rare, contain sensitive information, or may be proprietary. Here, we aim to explore how a set of broadly available covariates can describe typical seasonal subnational mobility in Kenya pre-COVID-19, therefore enabling better modelling of seasonal mobility across low- and middle-income country (LMIC) settings in non-pandemic settings. To do this, we used the Google Aggregated Mobility Research Dataset, containing anonymized mobility flows aggregated over users who have turned on the Location History setting, which is off by default. We combined this with socioeconomic and geospatial covariates from 2018 to 2019 to quantify seasonal changes in domestic and international mobility patterns across years. We undertook a spatiotemporal analysis within a Bayesian framework to identify relevant geospatial and socioeconomic covariates explaining human movement patterns, while accounting for spatial and temporal autocorrelations. Typical pre-pandemic mobility patterns in Kenya mostly consisted of shorter, within-county trips, followed by longer domestic travel between counties and international travel, which is important in establishing how mobility patterns changed post-pandemic. Mobility peaked in August and December, closely corresponding to school holiday seasons, which was found to be an important predictor in our model. We further found that socioeconomic variables including urbanicity, poverty, and female education strongly explained mobility patterns, in addition to geospatial covariates such as accessibility to major population centres and temperature. These findings derived from novel data sources elucidate broad spatiotemporal patterns of how populations move within and beyond Kenya, and can be easily generalized to other LMIC settings before the COVID-19 pandemic. Understanding such pre-pandemic mobility patterns provides a crucial baseline to interpret both how these patterns have changed as a result of the pandemic, as well as whether human mobility patterns have been permanently altered once the pandemic subsides. Our findings outline key correlates of mobility using broadly available covariates, alleviating the data bottlenecks of highly sensitive and proprietary mobile phone datasets, which many researchers do not have access to. These results further provide novel insight on monitoring mobility proxies in the context of disease surveillance and control efforts through LMIC settings.

Assessing the Effect of Global Travel and Contact Restrictions on Mitigating the COVID-19 Pandemic.

Travel restrictions and physical distancing have been implemented across the world to mitigate the coronavirus disease 2019 (COVID-19) pandemic, but studies are needed to understand their effectiveness across regions and time. Based on the population mobility metrics derived from mobile phone geolocation data across 135 countries or territories during the first wave of the pandemic in 2020, we built a metapopulation epidemiological model to measure the effect of travel and contact restrictions on containing COVID-19 outbreaks across regions. We found that if these interventions had not been deployed, the cumulative number of cases could have shown a 97-fold (interquartile range 79-116) increase, as of May 31, 2020. However, their effectiveness depended upon the timing, duration, and intensity of the interventions, with variations in case severity seen across populations, regions, and seasons. Additionally, before effective vaccines are widely available and herd immunity is achieved, our results emphasize that a certain degree of physical distancing at the relaxation of the intervention stage will likely be needed to avoid rapid resurgences and subsequent lockdowns.

Integrated vaccination and physical distancing interventions to prevent future COVID-19 waves in Chinese cities.

The coronavirus disease 2019 (COVID-19) pandemic has posed substantial challenges to the formulation of preventive interventions, particularly since the effects of physical distancing measures and upcoming vaccines on reducing susceptible social contacts and eventually halting transmission remain unclear. Here, using anonymized mobile geolocation data in China, we devise a mobility-associated social contact index to quantify the impact of both physical distancing and vaccination measures in a unified way. Building on this index, our epidemiological model reveals that vaccination combined with physical distancing can contain resurgences without relying on stay-at-home restrictions, whereas a gradual vaccination process alone cannot achieve this. Further, for cities with medium population density, vaccination can reduce the duration of physical distancing by 36% to 78%, whereas for cities with high population density, infection numbers can be well-controlled through moderate physical distancing. These findings improve our understanding of the joint effects of vaccination and physical distancing with respect to a city's population density and social contact patterns.

Uncovering two phases of early intercontinental COVID-19 transmission dynamics.

BACKGROUND: The COVID-19 pandemic has posed an ongoing global crisis, but how the virus spread across the world remains poorly understood. This is of vital importance for informing current and future pandemic response strategies. METHODS: We performed two independent analyses, travel network-based epidemiological modelling and Bayesian phylogeographic inference, to investigate the intercontinental spread of COVID-19. RESULTS: Both approaches revealed two distinct phases of COVID-19 spread by the end of March 2020. In the first phase, COVID-19 largely circulated in China during mid-to-late January 2020 and was interrupted by containment measures in China. In the second and predominant phase extending from late February to mid-March, unrestricted movements between countries outside of China facilitated intercontinental spread, with Europe as a major source. Phylogenetic analyses also revealed that the dominant strains circulating in the USA were introduced from Europe. However, stringent restrictions on international travel across the world since late March have substantially reduced intercontinental transmission. CONCLUSIONS: Our analyses highlight that heterogeneities in international travel have shaped the spatiotemporal characteristics of the pandemic. Unrestricted travel caused a large number of COVID-19 exportations from Europe to other continents between late February and mid-March, which facilitated the COVID-19 pandemic. Targeted restrictions on international travel from countries with widespread community transmission, together with improved capacity in testing, genetic sequencing and contact tracing, can inform timely strategies for mitigating and containing ongoing and future waves of COVID-19 pandemic.

Effect of non-pharmaceutical interventions for containing the COVID-19 outbreak in China.

BACKGROUND: The COVID-19 outbreak containment strategies in China based on non-pharmaceutical interventions (NPIs) appear to be effective. Quantitative research is still needed however to assess the efficacy of different candidate NPIs and their timings to guide ongoing and future responses to epidemics of this emerging disease across the World. METHODS: We built a travel network-based susceptible-exposed-infectious-removed (SEIR) model to simulate the outbreak across cities in mainland China. We used epidemiological parameters estimated for the early stage of outbreak in Wuhan to parameterise the transmission before NPIs were implemented. To quantify the relative effect of various NPIs, daily changes of delay from illness onset to the first reported case in each county were used as a proxy for the improvement of case identification and isolation across the outbreak. Historical and near-real time human movement data, obtained from Baidu location-based service, were used to derive the intensity of travel restrictions and contact reductions across China. The model and outputs were validated using daily reported case numbers, with a series of sensitivity analyses conducted. RESULTS: We estimated that there were a total of 114,325 COVID-19 cases (interquartile range [IQR] 76,776 - 164,576) in mainland China as of February 29, 2020, and these were highly correlated (p<0.001, R2=0.86) with reported incidence. Without NPIs, the number of COVID-19 cases would likely have shown a 67-fold increase (IQR: 44 - 94), with the effectiveness of different interventions varying. The early detection and isolation of cases was estimated to prevent more infections than travel restrictions and contact reductions, but integrated NPIs would achieve the strongest and most rapid effect. If NPIs could have been conducted one week, two weeks, or three weeks earlier in China, cases could have been reduced by 66%, 86%, and 95%, respectively, together with significantly reducing the number of affected areas. However, if NPIs were conducted one week, two weeks, or three weeks later, the number of cases could have shown a 3-fold, 7-fold, and 18-fold increase across China, respectively. Results also suggest that the social distancing intervention should be continued for the next few months in China to prevent case numbers increasing again after travel restrictions were lifted on February 17, 2020. CONCLUSION: The NPIs deployed in China appear to be effectively containing the COVID-19 outbreak, but the efficacy of the different interventions varied, with the early case detection and contact reduction being the most effective. Moreover, deploying the NPIs early is also important to prevent further spread. Early and integrated NPI strategies should be prepared, adopted and adjusted to minimize health, social and economic impacts in affected regions around the World.

Assessing spread risk of Wuhan novel coronavirus within and beyond China, January-April 2020: a travel network-based modelling study.

Background: A novel coronavirus (2019-nCoV) emerged in Wuhan City, China, at the end of 2019 and has caused an outbreak of human-to-human transmission with a Public Health Emergency of International Concern declared by the World Health Organization on January 30, 2020. Aim: We aimed to estimate the potential risk and geographic range of Wuhan novel coronavirus (2019-nCoV) spread within and beyond China from January through to April, 2020. Methods: A series of domestic and international travel network-based connectivity and risk analyses were performed, by using de-identified and aggregated mobile phone data, air passenger itinerary data, and case reports. Results: The cordon sanitaire of Wuhan is likely to have occurred during the latter stages of peak population numbers leaving the city before Lunar New Year (LNY), with travellers departing into neighbouring cities and other megacities in China. We estimated that 59,912 air passengers, of which 834 (95% UI: 478 - 1349) had 2019-nCoV infection, travelled from Wuhan to 382 cities outside of mainland China during the two weeks prior to Wuhan's lockdown. The majority of these cities were in Asia, but major hubs in Europe, the US and Australia were also prominent, with strong correlation seen between predicted importation risks and reported cases. Because significant spread has already occurred, a large number of airline travellers (3.3 million under the scenario of 75% travel reduction from normal volumes) may be required to be screened at origin high-risk cities in China and destinations across the globe for the following three months of February to April, 2020 to effectively limit spread beyond its current extent. Conclusion: Further spread of 2019-nCoV within China and international exportation is likely to occur. All countries, especially vulnerable regions, should be prepared for efforts to contain the 2019-nCoV infection.

Activity-specific mobility of adults in a rural region of western Kenya.

Improving rural household access to resources such as markets, schools and healthcare can help alleviate poverty in low-income settings. Current models of geographic accessibility to various resources rarely take individual variation into account due to a lack of appropriate data, yet understanding mobility at an individual level is key to knowing how people access their local resources. Our study used both an activity-specific survey and GPS trackers to evaluate how adults in a rural area of western Kenya accessed local resources. We calculated the travel time and time spent at six different types of resource and compared the GPS and survey data to see how well they matched. We found links between several demographic characteristics and the time spent at different resources, and that the GPS data reflected the survey data well for time spent at some types of resource, but poorly for others. We conclude that demography and activity are important drivers of mobility, and a better understanding of individual variation in mobility could be obtained through the use of GPS trackers on a wider scale.

Effect of non-pharmaceutical interventions to contain COVID-19 in China.

On 11 March 2020, the World Health Organization (WHO) declared coronavirus disease 2019 (COVID-19) a pandemic1. The strategies based on non-pharmaceutical interventions that were used to contain the outbreak in China appear to be effective2, but quantitative research is still needed to assess the efficacy of non-pharmaceutical interventions and their timings3. Here, using epidemiological data on COVID-19 and anonymized data on human movement4,5, we develop a modelling framework that uses daily travel networks to simulate different outbreak and intervention scenarios across China. We estimate that there were a total of 114,325 cases of COVID-19 (interquartile range 76,776-164,576) in mainland China as of 29 February 2020. Without non-pharmaceutical interventions, we predict that the number of cases would have been 67-fold higher (interquartile range 44-94-fold) by 29 February 2020, and we find that the effectiveness of different interventions varied. We estimate that early detection and isolation of cases prevented more infections than did travel restrictions and contact reductions, but that a combination of non-pharmaceutical interventions achieved the strongest and most rapid effect. According to our model, the lifting of travel restrictions from 17 February 2020 does not lead to an increase in cases across China if social distancing interventions can be maintained, even at a limited level of an on average 25% reduction in contact between individuals that continues until late April. These findings improve our understanding of the effects of non-pharmaceutical interventions on COVID-19, and will inform response efforts across the world.

Exploring the use of mobile phone data for national migration statistics.

Statistics on internal migration are important for keeping estimates of subnational population numbers up-to-date as well as urban planning, infrastructure development and impact assessment, among other applications. However, migration flow statistics typically remain constrained by the logistics of infrequent censuses or surveys. The penetration rate of mobile phones is now high across the globe with rapid recent increases in ownership in low-income countries. Analysing the changing spatiotemporal distribution of mobile phone users through anonymized call detail records (CDRs) offers the possibility to measure migration at multiple temporal and spatial scales. Based on a dataset of 72 billion anonymized CDRs in Namibia from October 2010 to April 2014, we explore how internal migration estimates can be derived and modelled from CDRs at subnational and annual scales, and how precision and accuracy of these estimates compare to census-derived migration statistics. We also demonstrate the use of CDRs to assess how migration patterns change over time, with a finer temporal resolution compared to censuses. Moreover, we show how gravity-type spatial interaction models built using CDRs can accurately capture migration flows. Results highlight that estimates of migration flows made using mobile phone data is a promising avenue for complementing more traditional national migration statistics and obtaining more timely and local data.

Using parasite genetic and human mobility data to infer local and cross-border malaria connectivity in Southern Africa.

Local and cross-border importation remain major challenges to malaria elimination and are difficult to measure using traditional surveillance data. To address this challenge, we systematically collected parasite genetic data and travel history from thousands of malaria cases across northeastern Namibia and estimated human mobility from mobile phone data. We observed strong fine-scale spatial structure in local parasite populations, providing positive evidence that the majority of cases were due to local transmission. This result was largely consistent with estimates from mobile phone and travel history data. However, genetic data identified more detailed and extensive evidence of parasite connectivity over hundreds of kilometers than the other data, within Namibia and across the Angolan and Zambian borders. Our results provide a framework for incorporating genetic data into malaria surveillance and provide evidence that both strengthening of local interventions and regional coordination are likely necessary to eliminate malaria in this region of Southern Africa.

Changing epidemiology and challenges of malaria in China towards elimination.

BACKGROUND: Historically, malaria had been a widespread disease in China. A national plan was launched in China in 2010, aiming to eliminate malaria by 2020. In 2017, no indigenous cases of malaria were detected in China for the first time. To provide evidence for precise surveillance and response to achieve elimination goal, a comprehensive study is needed to determine the changing epidemiology of malaria and the challenges towards elimination. METHODS: Using malaria surveillance data from 2011 to 2016, an integrated series of analyses was conducted to elucidate the changing epidemiological features of autochthonous and imported malaria, and the spatiotemporal patterns of malaria importation from endemic countries. RESULTS: From 2011 to 2016, a total of 21,062 malaria cases with 138 deaths were reported, including 91% were imported and 9% were autochthonous. The geographic distribution of local transmission have shrunk dramatically, but there were still more than 10 counties reporting autochthonous cases in 2013-2016, particularly in counties bordering with countries in South-East Asia. The importation from 68 origins countries had an increasing annual trend from Africa but decreasing importation from Southeast Asia. Four distinct communities have been identified in the importation networks with the destinations in China varied by origin and species. CONCLUSIONS: China is on the verge of malaria elimination, but the residual transmission in border regions and the threats of importation from Africa and Southeast Asia are the key challenges to achieve and maintain malaria elimination. Efforts from China are also needed to help malaria control in origin countries and reduce the risk of introduced transmission.

Measuring mobility, disease connectivity and individual risk: a review of using mobile phone data and mHealth for travel medicine.

RATIONALE FOR REVIEW: The increasing mobility of populations allows pathogens to move rapidly and far, making endemic or epidemic regions more connected to the rest of the world than at any time in history. However, the ability to measure and monitor human mobility, health risk and their changing patterns across spatial and temporal scales using traditional data sources has been limited. To facilitate a better understanding of the use of emerging mobile phone technology and data in travel medicine, we reviewed relevant work aiming at measuring human mobility, disease connectivity and health risk in travellers using mobile geopositioning data. KEY FINDINGS: Despite some inherent biases of mobile phone data, analysing anonymized positions from mobile users could precisely quantify the dynamical processes associated with contemporary human movements and connectivity of infectious diseases at multiple temporal and spatial scales. Moreover, recent progress in mobile health (mHealth) technology and applications, integrating with mobile positioning data, shows great potential for innovation in travel medicine to monitor and assess real-time health risk for individuals during travel. CONCLUSIONS: Mobile phones and mHealth have become a novel and tremendously powerful source of information on measuring human movements and origin-destination-specific risks of infectious and non-infectious health issues. The high penetration rate of mobile phones across the globe provides an unprecedented opportunity to quantify human mobility and accurately estimate the health risks in travellers. Continued efforts are needed to establish the most promising uses of these data and technologies for travel health.

Exploring fine-scale human and livestock movement in western Kenya.

Human and livestock mobility are key factors in the transmission of several high-burden zoonoses such as rift valley fever and trypanosomiasis, yet our knowledge of this mobility is relatively poor due to difficulty in quantifying population-level movement patterns. Significant variation in the movement patterns of individual hosts means it is necessary to capture their fine-scale mobility in order to gain useful knowledge that can be extrapolated to a population level. Here we explore how the movements of people and their ruminants, and their exposure to various types of land cover, correlate with ruminant ownership and other demographic factors which could affect individual exposure to zoonoses. The study was conducted in Busia County, western Kenya, where the population are mostly subsistence farmers operating a mixed crop/livestock farming system. We used GPS trackers to collect movement data from 26 people and their ruminants for 1 week per individual in July/August 2016, and the study was repeated at the end of the same year to compare movement patterns between the short rainy and dry seasons respectively. We found that during the dry season, people and their ruminants travelled further on trips outside of the household, and that people spent less time on swampland compared to the short rainy season. Our findings also showed that ruminant owners spent longer and travelled further on trips outside the household than non-ruminant owners, and that people and ruminants from poorer households travelled further than people from relatively wealthier households. These results indicate that some individual-level mobility may be predicted by season and by household characteristics such as ruminant ownership and household wealth, which could have practical uses for assessing individual risk of exposure to some zoonoses and for future modelling studies of zoonosis transmission in similar rural areas.

Estimating uncertainty in geospatial modelling at multiple spatial resolutions: the pattern of delivery via caesarean section in Tanzania.

Visualising maternal and newborn health (MNH) outcomes at fine spatial resolutions is crucial to ensuring the most vulnerable women and children are not left behind in improving health. Disaggregated data on life-saving MNH interventions remain difficult to obtain, however, necessitating the use of Bayesian geostatistical models to map outcomes at small geographical areas. While these methods have improved model parameter estimates and precision among spatially correlated health outcomes and allowed for the quantification of uncertainty, few studies have examined the trade-off between higher spatial resolution modelling and how associated uncertainty propagates. Here, we explored the trade-off between model outcomes and associated uncertainty at increasing spatial resolutions by quantifying the posterior distribution of delivery via caesarean section (c-section) in Tanzania. Overall, in modelling delivery via c-section at multiple spatial resolutions, we demonstrated poverty to be negatively correlated across spatial resolutions, suggesting important disparities in obtaining life-saving obstetric surgery persist across sociodemographic factors. Lastly, we found that while uncertainty increased with higher spatial resolution input, model precision was best approximated at the highest spatial resolution, suggesting an important policy trade-off between identifying concealed spatial heterogeneities in health indicators.