Local Population Density Research Articles

Assessment of health facility quality improvements, United Republic of Tanzania.

ObjectiveTo identify contextual factors associated with quality improvements in primary health-care facilities in the United Republic of Tanzania between two star rating assessments, focusing on local district administration and proximity to other facilities.MethodsFacilities underwent star rating assessments in 2015 and between 2017 and 2018; quality was rated from zero to five stars. The consolidated framework for implementation research, adapted to a low-income context, was used to identify variables associated with star rating improvements between assessments. Facility data were obtained from several secondary sources. The proportion of the variance in facility improvement observed at facility and district levels and the influence of nearby facilities and district administration were estimated using multilevel regression models and a hierarchical spatial autoregressive model, respectively.FindingsStar ratings improved at 4028 of 5595 (72%) primary care facilities. Factors associated with improvement included: (i) star rating in 2015; (ii) facility type (e.g. hospital) and ownership (e.g. public); (iii) participation in, or eligibility for, a results-based financing programme; (iv) local population density; and (v) distance from a major road. Overall, 20% of the variance in facility improvement was associated with district administration. Geographical clustering indicated that improvement at a facility was also associated with improvements at nearby facilities.ConclusionAlthough the majority of facilities improved their star rating, there were substantial variations between facilities. Both district administration and proximity to high-performing facilities influenced improvements. Quality improvement interventions should take advantage of factors operating above the facility level, such as peer learning and peer pressure.

Socio-Economic Disparities and COVID-19 in the USA

COVID-19 is not a universal killer. We study the spread of COVID-19 at the county level for the United States up until the 15$^{th}$ of August, 2020. We show that the prevalence of the disease and the death rate are correlated with the local socio-economic conditions often going beyond local population density distributions, especially in rural areas. We correlate the COVID-19 prevalence and death rate with data from the US Census Bureau and point out how the spreading patterns of the disease show asymmetries in urban and rural areas separately and are preferentially affecting the counties where a large fraction of the population is non-white. Our findings can be used for more targeted policy building and deployment of resources for future occurrence of a pandemic due to SARS-CoV-2. Our methodology, based on interpretable machine learning and game theory, can be extended to study the spread of other diseases.

Examining non-linear associations between population density and waist-hip ratio: An application of gradient boosting decision trees

Many studies have explored the relationship between population density and obesity, but there is no consensus, particularly in dense Chinese cities. This study applied gradient boosting decision trees to 2014 national survey data to examine the non-linear or threshold effects of population density at both local and regional levels on waist-hip ratio (WHR), controlling for other built environment elements and socio-demographics. Built environment elements collectively have a stronger predictive power than socio-demographics (56.6% vs. 43.4%). Within a certain range, regional population density is negatively associated with WHR, but its marginal effect diminishes beyond the upper threshold. Local population density has a U-shaped relationship with WHR. These results suggest that urban planners can alleviate the risk of obesity through population densification, but over-densification tends to be inefficient, and sometimes counterproductive.

Dispersal evolution diminishes the negative density dependence in dispersal.

In many organisms, dispersal varies with the local population density. Such patterns of density-dependent dispersal (DDD) are expected to shape the dynamics, spatial spread, and invasiveness of populations. Despite their ecological importance, empirical evidence for the evolution of DDD patterns remains extremely scarce. This is especially relevant because rapid evolution of dispersal traits has now been empirically confirmed in several taxa. Changes in DDD of dispersing populations could help clarify not only the role of DDD in dispersal evolution, but also the possible pattern of subsequent range expansion. Here, we investigate the relationship between dispersal evolution and DDD using a long-term experimental evolution study on Drosophila melanogaster. We compared the DDD patterns of four dispersal-selected populations and their non-selected controls. The control populations showed negative DDD, which was stronger in females than in males. In contrast, the dispersal-selected populations showed DDD, where neither males nor females exhibited DDD. We compare our results with previous evolutionary predictions that focused largely on positive DDD, and highlight how the direction of evolutionary change depends on the initial DDD pattern of a population. Finally, we discuss the implications of DDD evolution for spatial ecology and evolution.

Estimating small-area population density in Sri Lanka using surveys and Geo-spatial data.

Country-level census data are typically collected once every 10 years. However, conflicts, migration, urbanization, and natural disasters can rapidly shift local population patterns. This study demonstrates the feasibility of a “bottom-up”-method to estimate local population density in the between-census years by combining household surveys with contemporaneous geo-spatial data, including village-area and satellite imagery-based indicators. We apply this technique to the case of Sri Lanka using Poisson regression models based on variables selected using the Least Absolute Shrinkage and Selection Operator (LASSO). The model is estimated in villages sampled in the 2012/13 Household Income and Expenditure Survey, and is employed to obtain out-of-sample density estimates in the non-surveyed villages. These estimates approximate the census density accurately and are more precise than other bottom-up studies using similar geo-spatial data. While most open-source population products redistribute census population “top-down” from higher to lower spatial units using areal interpolation and dasymetric mapping techniques, these products become less accurate as the census itself ages. Our method circumvents the problem of the aging census by relying instead on more up-to-date household surveys. The collective evidence suggests that our method is cost effective in tracking local population density with greater frequency in the between-census years.

Variation in space and time: a long-term examination of density-dependent dispersal in a woodland rodent.

Dispersal is a fundamental ecological process that can be affected by population density, yet studies report contrasting effects of density on propensity to disperse. In addition, the relationship between dispersal and density is seldom examined using densities measured at different spatial scales or over extensive time series. We used 51 years of trapping data to examine how dispersal by wild deer mice (Peromyscus maniculatus) was affected by changes in both local and regional population densities. We examined these patterns over both the entire time series and also in 10-year shifting windows to determine whether the nature and strength of the relationship changed through time. Probability of dispersal decreased with increased local and regional population density, and the negative effect of local density on dispersal was more pronounced in years with low regional densities. In addition, the strength of negative density-dependent dispersal changed through time, ranging from very strong in some decades to absent in other periods of the study. Finally, while females were less likely to disperse, female dispersal was more density-dependent than male dispersal. Our study shows that the relationship between density and dispersal is not temporally static and that investigations of density-dependent dispersal should consider both local and regional population densities.

Mathematical analysis of prey predator system with immigrant prey using a new approach to Homotopy perturbation method

A mathematical model for predator prey system with immigrant prey previously proposed is solved analytically to get an approximate analytical solution for local prey population density, immigrant prey population density and the predator population density. The analytical solution derived is compared with the numerical solution obtained using MATLAB and is found to make a good fit. The derived results have been illustrated with their graphs for certain parametric values.

Correlation between COVID-19 Morbidity and Mortality Rates in Japan and Local Population Density, Temperature, and Absolute Humidity.

This study analyzed the morbidity and mortality rates of the coronavirus disease (COVID-19) pandemic in different prefectures of Japan. Under the constraint that daily maximum confirmed deaths and daily maximum cases should exceed 4 and 10, respectively, 14 prefectures were included, and cofactors affecting the morbidity and mortality rates were evaluated. In particular, the number of confirmed deaths was assessed, excluding cases of nosocomial infections and nursing home patients. The correlations between the morbidity and mortality rates and population density were statistically significant (p-value < 0.05). In addition, the percentage of elderly population was also found to be non-negligible. Among weather parameters, the maximum temperature and absolute humidity averaged over the duration were found to be in modest correlation with the morbidity and mortality rates. Lower morbidity and mortality rates were observed for higher temperature and absolute humidity. Multivariate linear regression considering these factors showed that the adjusted determination coefficient for the confirmed cases was 0.693 in terms of population density, elderly percentage, and maximum absolute humidity (p-value < 0.01). These findings could be useful for intervention planning during future pandemics, including a potential second COVID-19 outbreak.

Nonuniform impacts of COVID-19 lockdown on air quality over the United States

Most of the state governments in United States (U.S.) issued lockdown or business restrictions amid the COVID-19 pandemic in March 2020, which created a unique opportunity to evaluate the air quality response to reduced economic activities. Data acquired from 28 long-term air quality stations across the U.S. revealed widespread but nonuniform reductions of nitrogen dioxide (NO2) and carbon monoxide (CO) during the first phase of lockdown (March 15–April 25, 2020) relative to a pre-lockdown reference period and historical baselines established in 2017–2019. The reductions, up to 49% for NO2 and 37% for CO, are statistically significant at two thirds of the sites and tend to increase with local population density. Significant reductions of particulate matter (PM2.5 and PM10) only occurred in the Northeast and California/Nevada metropolises where NO2 declined the most, while the changes in ozone (O3) were mixed and relatively minor. These findings are consistent with lower transportation and utility demands that dominate NO2 and CO emissions, especially in major urban areas, due to the lockdown. This study provides an insight into potential public health benefits with more aggressive air quality management, which should be factored into strategies to reopen the U.S. and global economy.

Modeling spatial access to cervical cancer screening services in Ondo State, Nigeria

BackgroundWomen in low- and middle-income countries (LMIC) remain at high risk of developing cervical cancer and have limited access to screening programs. The limits include geographical barriers related to road network characteristics and travel behaviors but these have neither been well studied in LMIC nor have methods to overcome them been incorporated into cervical cancer screening delivery programs.MethodsTo identify and evaluate spatial barriers to cervical cancer prevention services in Ondo State, Nigeria, we applied a Multi-Mode Enhanced Two-Step Floating Catchment Area model to create a spatial access index for cervical cancer screening services in Ondo City and the surrounding region. The model used inputs that included the distance between service locations and population centers, local population density, quantity of healthcare infrastructures, modes of transportation, and the travel time budgets of clients. Two different travel modes, taxi and mini bus, represented common modes of transit. Geocoded client residential locations were compared to spatial access results to identify patterns of spatial access and estimate where gaps in access existed.ResultsOndo City was estimated to have the highest access in the region, while the largest city, Akure, was estimated to be in only the middle tier of access. While 73.5% of clients of the hospital in Ondo City resided in the two highest access zones, 21.5% of clients were from locations estimated to be in the lowest access catchment, and a further 2.25% resided outside these limits. Some areas that were relatively close to cervical cancer screening centers had lower access values due to poor road network coverage and fewer options for public transportation.ConclusionsVariations in spatial access were revealed based on client residential patterns, travel time differences, distance decay assumptions, and travel mode choices. Assessing access to cervical cancer screening better identifies potentially underserved locations in rural Nigeria that can inform plans for cervical cancer screening including new or improved infrastructure, effective resource allocation, introduction of service options for areas with lower access, and design of public transportation networks.

Evaluating Methodology for the Service Extent of Refugee Parks in Changchun, China

Refugee parks are general parks that can serve as emergency shelters in cities. The core issue of refugee parks lies in their service extent they provided. Globally, the service extent of refugee parks is determined by the Euclidean or actual road network distance methods. The former lacks measurement accuracy, whereas the latter lacks the consideration of human dimension and proximity. Hence, we propose the nearest neighbor method, which considers not only the locations of refugee parks and sub-districts, but also road networks and census data. Using this method, we evaluated the service extent of refugee parks in Changchun, northern China. We compared our results with the Euclidean distance method. Results showed that the nearest neighbor method effectively accounted for the effect of road network resistance and results aligned with the refuge needs of residents. Differences in both methods were mainly affected by the size of the parks and local road network and population densities. The Euclidean approach determines the service extent based on a unified service radius, thus producing greater errors. The nearest neighbor method can reveal the spatial imbalance of refugee parks, as well as the mismatch between the park size and population distribution. Furthermore, the nearest neighbor method implements policies of spatial optimization of urban refugee parks. As a general method, it should be suited to different types of disasters.

Simulating the spread of COVID-19 via a spatially-resolved susceptible–exposed–infected–recovered–deceased (SEIRD) model with heterogeneous diffusion

We present an early version of a Susceptible–Exposed–Infected–Recovered–Deceased (SEIRD) mathematical model based on partial differential equations coupled with a heterogeneous diffusion model. The model describes the spatio-temporal spread of the COVID-19 pandemic, and aims to capture dynamics also based on human habits and geographical features. To test the model, we compare the outputs generated by a finite-element solver with measured data over the Italian region of Lombardy, which has been heavily impacted by this crisis between February and April 2020. Our results show a strong qualitative agreement between the simulated forecast of the spatio-temporal COVID-19 spread in Lombardy and epidemiological data collected at the municipality level. Additional simulations exploring alternative scenarios for the relaxation of lockdown restrictions suggest that reopening strategies should account for local population densities and the specific dynamics of the contagion. Thus, we argue that data-driven simulations of our model could ultimately inform health authorities to design effective pandemic-arresting measures and anticipate the geographical allocation of crucial medical resources.

Density-dependent responses of mountain pine beetle to its pheromones and host volatiles in naïve lodgepole pine stands

Mountain pine beetle has expanded its host range into naïve lodgepole pine forests in western Canada and killed a large number of pine trees. Synthetic attractants mainly bark beetle pheromones and host monoterpenes attached to traps are typically used to monitor beetle populations within its range. Despite well-established differences in beetle host selection behavior between endemic and outbreak population densities, the influence of population density on the response of mountain pine beetle to the synthetic attractants is poorly understood. Over three years, we tested the effects of different release rates of female mountain pine beetle aggregation pheromone (trans-verbenol) and two host monoterpenes (terpinolene and mycrene) and local beetle population density on trap catches of mountain pine beetle. We estimated local mountain pine beetle population densities using the number of dead trees observed in the previous year’s aerial detection survey. During the first three weeks of the flight period, the lures with low release rates of all components captured more mountain pine beetle with increasing beetle population density and the lures with a high release rate of trans-verbenol plus a low release rate of host monoterpenes were preferred by beetles in low population density sites. We also found a temporal shift in attraction patterns. As the flight period progressed, the preference for low trans-verbenol release rate lures was less pronounced at high mountain pine beetle densities. No sex-specific responses of mountain pine beetles, along with number of competitor and predatory beetles of mountain pine beetle, were found for a particular attractant. These results provide evidence that mountain pine beetle has a density dependent response to attractants and can assess available semiochemicals cues to ensure successful host colonization depending on their density and the flight period. Overall, this study can improve the efficacy of current semiochemical-based monitoring tools, which are essential to implementing informed mitigation strategies for mountain pine beetle especially while they are in a sub-outbreak stage in the expanded distribution.

Black widows as plastic wallflowers: female choosiness increases with indicators of high mate availability in a natural population

Female choice is an important driver of sexual selection, but can be costly, particularly when choosy females risk remaining unmated or experience delays to reproduction. Thus, females should reduce choosiness when mate encounter rates are low. We asked whether choosiness is affected by social context, which may provide reliable information about the local availability of mates. This has been demonstrated in the lab, but rarely under natural conditions. We studied western black widow spiders (Latrodectus hesperus) in the field, placing experimental final-instar immature females so they were either ‘isolated’ or ‘clustered’ near naturally occurring conspecifics (≥10 m or ≤1 m, respectively, from a microhabitat occupied by at least one other female). Upon maturity, females in both treatments were visited by similar numbers of males, but clustered females were visited by males earlier and in more rapid succession than isolated females, confirming that proximity to conspecifics reduces the risk of remaining unmated. As predicted, isolated females were less choosy in staged mating trials, neither rejecting males nor engaging in pre-copulatory cannibalism, in contrast to clustered females. These results demonstrate that exposure of females to natural variation in demography in the field can alter choosiness of adults. Thus, female behaviour in response to cues of local population density can affect the intensity of sexual selection on males in the wild.

Epidemiological Characteristics of Novel Coronavirus COVID-19 Based on Web Data Mining

Based on the Selenium data mining technology, the epidemiological characteristics of real help cases in Sina Weibo were obtained by the analysis of 690 valid cases posted in the Sina Weibo “Pneumonia Patients Asking for Help” topic from February 4 to February 22, 2020. The research showed that 97.6% of the patients seeking for help came from Wuhan, mainly centralized in Wuchang, Tongkou, Hanyang etc. urban areas, and the proportion is directly proportional to the local medical resources and population density. The cases of Weibo help were mainly distributed from February 4 to February 7, 2020. With the relief of medical resources, the number of cases seeking help through social media decreased significantly. The distribution of patients, whose diagnosed date was mainly from January 16 to February 6, 2020, was basically consistent with the case information released by the Chinese Center for Disease Control and Prevention (CCDC). The median age of patients seeking for help was 60 years old, which was much higher than the data released by the CCDC but was roughly coincident with the data of the central hospital of Wuhan. The results of this study indicate that when dealing with major outbreaks of infectious diseases, social media are equally important in epidemiological analysis as well as the role in the dissemination of public opinion. Based on the wide adoption and timeliness nature of social media, it will be helpful for decision-makers to quickly grasp the real-world situation as it is combined with data mining or big data analysis.

Local economic structure, regional competition and the formation of industrial land price in China: Combining evidence from process tracing with quantitative results

The price of industrial land in China has been found distorted and remarkably low. However, it is overlooked that industrial land price is relatively high in some regions. This local variation cannot be explained by classical theories on land price that focus on local economic level, population density, and location factors. We propose a theoretical framework incorporating local economic structure and governments’ behavior in regional competition, to interpret the formation of industrial land price in China. We first model local firms as foot-tight ones, whose relocation costs are enormous, and outside firms as foot-loose ones, whose relocation costs are negligible. Then we divide local governments as outside-capital-dependent governments (OCDGs) and non-outside-capital-dependent governments (NOCDGs) according to the role of outside capital in local economic structure. In such a setting, OCDGs are supposed to aggressively pursue outside firms and use industrial land as a critical endowment to engage in race-to-bottom competition, making the price extremely low. On the contrary, the optimal strategy for NOCDGs, who lack strong incentives in attracting outside investment, is to stay aside and let potential land users compete to determine the land price, resulting in a higher land price. Evidence from quantitative results and comparative case studies with process tracing based on Suzhou and Wenzhou together prove the validity of this theory. This paper advances the conventional understanding of industrial land price and concludes with implications on industrial land policies and sustainable development.

Modelling transition in land cover highlights forest losses and gains in Southeast Asia

Forest ecosystems in Southeast Asia are currently facing multiple challenges, including threats caused by global trade or local factors such as land conversion. It is therefore crucial to have a better understanding of the mechanisms driving deforestation and forest recovery. In the present study, the losses and gains of forest were quantified over a 27 year period with 300 m-resolution land cover data and data on human population densities over 19 years covering a large area of continental Southeast Asia. The forest dynamics was characterised by different phases that were largely synchronised among countries. The rate of deforestation was related to population density in core forest but not in edge forest. In spite of different national dynamics, all countries showed two concomitant trends: a decrease in the rates of forest loss over the years, and a decrease in the impact of human population density on the rates of forest loss. The importance of local population density for deforestation in core forest have some consequences for management and forest conservation suggesting that migration and local settlements are important drivers of local deforestation in Southeast Asia.

Fluctuating asymmetry in Alpine chamois horns: an indicator of environmental stress

Developmental stability of an individual is often evaluated by means of fluctuating asymmetry (FA) in bilaterally paired morphological characters. Even though FA has been widely investigated in ungulates, its connection with the condition of individuals and their environment is still debated. In this study we investigated factors contributing to FA in horn length in the sexually monomorphic Alpine chamois. We measured right and left horn length of 1682 Alpine chamois (Nfemales = 734; Nmales = 948) shot during 2 consecutive hunting seasons (2015 and 2016) in 7 neighbouring districts in Central-Eastern Alps (Italy). We found no consistent left or right bias. Within our study population, FA values were normally distributed around a mean value that was not significantly different from zero (Skewness = − 0.107, SE = 0.06; Kurtosis = − 0.055, SE = 0.119). We also found that absolute FA in horn length was affected by environmental and climatic conditions experienced by the individuals during their first year and half of life. Statistically significant differences between right and left horn length were found with higher local population density and lower forage quality (i.e., siliceous substrate). Moreover, snow cover duration during the individuals’ first winter increased horn length asymmetry. No individual characteristics played a role in promoting horn length asymmetry. The associations between exposure to stressors and deviations from bilateral symmetry suggest that absolute FA can be used to identify populations whose individuals experienced stressful conditions early in life. We found in this relatively monomorphic species that both male and female horns were equally affected by climate, substrate, and local population density, thus showing that large male secondary sexual characters, such as the antlers of deer stags, are not the only traits which can be influenced by a negative environment and exhibit increasing FA.

Racial Disparities in Neonatal Mortality Among Both Singleton and Multiple Gestations [32O

INTRODUCTION: Previous studies have noted racial disparities in neonatal mortality. This study seeks to clarify the extent to which multiple gestation moderates the association between race and mortality risk. METHODS: Kid's Inpatient Database (2016), which includes a nationally representative sample of all births, was used for this analysis. A multivariate logistic regression controlling for hospital-level factors (i.e. region, size, ownership, teaching status, and local population density) and patient insurance type was used to assess the interaction between race and neonatal mortality among liveborn singleton (n=2,682,478) and twin (n=82,201) deliveries without congenital anomalies. Neonatal death among twins was defined as at least one neonatal death within each pair. RESULTS: White twin neonates (n=50,003) had 4.86 times the odds of death (95% CI 4.31, 5.48) compared to white singleton neonates (n=1,523,110). Black neonates had twice the odds of death compared to white singleton neonates (n=398,006; 95% CI 1.88, 2.25) and twin neonates (n=14,569; 95% CI 1.67, 2.40). Hispanic neonates also had an elevated mortality risk in both singleton (n=589,260; aOR 1.20; 95% CI 1.03, 1.27) and twin (n=12,927; aOR 1.51; 95% CI 1.10, 1.68) contexts relative to whites. Mortality among Asian singletons (n=172,103) was not significantly different from white singletons, but Asian twins (n=4,702) had 1.56 times the odds of death (95% CI 1.08, 2.25) compared to white twins. CONCLUSION: Black and Hispanic singleton and twin neonates without congenital anomalies had higher mortality rates compared to white neonates. The association between race and neonatal mortality is further moderated by multiple gestations.

Optimal spatial prioritization of control resources for elimination of invasive species under demographic uncertainty.

Populations of invasive species often spread heterogeneously across a landscape, consisting of local populations that cluster in space but are connected by dispersal. A fundamental dilemma for invasive species control is how to optimally allocate limited fiscal resources across local populations. Theoretical work based on perfect knowledge of demographic connectivity suggests that targeting local populations from which migrants originate (sources) can be optimal. However, demographic processes such as abundance and dispersal can be highly uncertain, and the relationship between local population density and damage costs (damage function) is rarely known. We used a metapopulation model to understand how budget and uncertainty in abundance, connectivity, and the damage function, together impact return on investment (ROI) for optimal control strategies. Budget, observational uncertainty, and the damage function had strong effects on the optimal resource allocation strategy. Uncertainty in dispersal probability was the least important determinant of ROI. The damage function determined which resource prioritization strategy was optimal when connectivity was symmetric but not when it was asymmetric. When connectivity was asymmetric, prioritizing source populations had a higher ROI than allocating effort equally across local populations, regardless of the damage function, but uncertainty in connectivity structure and abundance reduced ROI of the optimal prioritization strategy by 57% on average depending on the control budget. With low budgets (monthly removal rate of 6.7% of population), there was little advantage to prioritizing resources, especially when connectivity was high or symmetric, and observational uncertainty had only minor effects on ROI. Allotting funding for improved monitoring appeared to be most important when budgets were moderate (monthly removal of 13-20% of the population). Our result showed that multiple sources of observational uncertainty should be considered concurrently for optimizing ROI. Accurate estimates of connectivity direction and abundance were more important than accurate estimates of dispersal rates. Developing cost-effective surveillance methods to reduce observational uncertainties, and quantitative frameworks for determining how resources should be spatially apportioned to multiple monitoring and control activities are important and challenging future directions for optimizing ROI for invasive species control programs.