Global poverty estimation using private and public sector big data sources.

Household surveys give a precise estimate of poverty; however, surveys are costly and are fielded infrequently. We demonstrate the importance of jointly using multiple public and private sector data sources to estimate levels and changes in wealth for a large set of countries. We train models using 63,854 survey cluster locations across 59 countries, relying on data from satellites, Facebook Marketing information, and OpenStreetMaps. The model generalizes previous approaches to a wide set of countries. On average, across countries, the model explains 55% (min = 14%; max = 85%) of the variation in levels of wealth at the survey cluster level and 59% (min = 0%; max = 93%) of the variation at the district level, and the model explains 4% (min = 0%; max = 17%) and 6% (min = 0%; max = 26%) of the variation of changes in wealth at the cluster and district levels. Models perform best in lower-income countries and in countries with higher variance in wealth. Features from nighttime lights, OpenStreetMaps, and land cover data are most important in explaining levels of wealth, and features from nighttime lights are most important in explaining changes in wealth.

The evolution of the COVID-19 pandemic through the lens of google searches.

Real-time data is essential for policymakers to adapt to a rapidly evolving situation like the COVID-19 pandemic. Using data from 221 countries and territories, we demonstrate the capacity of Google search data to anticipate reported COVID-19 cases and understand how containment policies are associated with changes in socioeconomic indicators. First, search interest in COVID-specific symptoms such as "loss of smell" strongly correlated with cases initially, but the association diminished as COVID-19 evolved; general terms such as "COVID symptoms" remained strongly associated with cases. Moreover, trends in search interest preceded trends in reported cases, particularly in the first year of the pandemic. Second, countries with more restrictive containment policies experienced greater search interest in unemployment and mental health terms after policies were implemented, indicating socio-economic externalities. Higher-income countries experienced a larger increase in searches related to unemployment and a larger reduction in relationship and family planning keywords relative to lower-income countries. The results demonstrate that real-time search interest can be a valuable tool to inform policies across multiple stages of the pandemic.

Applying machine learning and geolocation techniques to social media data (Twitter) to develop a resource for urban planning.

With all the recent attention focused on big data, it is easy to overlook that basic vital statistics remain difficult to obtain in most of the world. What makes this frustrating is that private companies hold potentially useful data, but it is not accessible by the people who can use it to track poverty, reduce disease, or build urban infrastructure. This project set out to test whether we can transform an openly available dataset (Twitter) into a resource for urban planning and development. We test our hypothesis by creating road traffic crash location data, which is scarce in most resource-poor environments but essential for addressing the number one cause of mortality for children over five and young adults. The research project scraped 874,588 traffic related tweets in Nairobi, Kenya, applied a machine learning model to capture the occurrence of a crash, and developed an improved geoparsing algorithm to identify its location. We geolocate 32,991 crash reports in Twitter for 2012-2020 and cluster them into 22,872 unique crashes during this period. For a subset of crashes reported on Twitter, a motorcycle delivery service was dispatched in real-time to verify the crash and its location; the results show 92% accuracy. To our knowledge this is the first geolocated dataset of crashes for the city and allowed us to produce the first crash map for Nairobi. Using a spatial clustering algorithm, we are able to locate portions of the road network (<1%) where 50% of the crashes identified occurred. Even with limitations in the representativeness of the data, the results can provide urban planners with useful information that can be used to target road safety improvements where resources are limited. The work shows how twitter data might be used to create other types of essential data for urban planning in resource poor environments.

Infection dynamics of Bonamia exitiosa on intertidal Ostrea angasi farms.

Bonamia spp. cause epizootics in oysters worldwide. In southern Australia, Bonamia exitiosa Hine, Cochennac and Berthe, 2001 threatens aquaculture of Ostrea angasi Sowerby, 1871. Bonamia spp. infections can display strong seasonality, but seasonal dynamics of B. exitiosa-O. angasi are unknown. Ostrea angasi naïve to B. exitiosa infection were stocked onto farms in three growing regions, and B. exitiosa was monitored seasonally for one year. Environmental parameters we measured did not correlate with B. exitiosa prevalence or infection intensities. Extreme temperatures suggest O. angasi culture systems need development. Bonamia exitiosa prevalence increased over time. After three months, O. angasi had B. exitiosa prevalence of 0.08-0.4, and after one year, the prevalence was 0.57-0.88. At some sites, O. angasi had >0.5 B. exitiosa prevalence in >6 months, but at other sites, >9 months passed before prevalence was >0.5. Bonamia exitiosa infection intensities were low with no seasonal pattern but were affected by the interaction of site, season and oyster meat:shell ratio. Understanding infection and initiating a breeding programme for resistance would provide benefits for O. angasi industry expansion.

Rapid transmission of Bonamia exitiosa by cohabitation causes mortality in Ostrea angasi.

The haplosporidian Bonamia was first detected in Australian shellfish in 1991. Australian isolates in Ostrea angasi Sowerby, 1871 were identified as Bonamia exitiosa Hine, Cochennac and Berthe, 2001, which threatens development of an O. angasi aquaculture industry. European field data suggest that Bonamia ostreae Pichot, Comps, Tigé, Grizel and Rabouin, 1980 infections in Ostrea edulis Linnaeus, 1758 build slowly, but infection dynamics of B. exitiosa in O. angasi are unknown. We investigated B. exitiosa infection in O. angasi by cohabiting uninfected juvenile O. angasi with adults infected with B. exitiosa. Oysters were sampled at 10, 21 and 40 days after cohabitation, and B. exitiosa prevalence and intensity were assessed. Bonamia exitiosa rapidly infected and caused disease in O. angasi. Mortalities began at 12 days, with ˜50% mortality by day 21 and >85% mortality by day 40. Mortalities displayed pathology consistent with clinical B. exitiosa infection. Time to first infection is likely influenced by a combination of parasite infectivity, host exposure and host immune capacity. Host death is not required for transmission, but probably facilitates release of parasites from decaying tissue. Understanding B. exitiosa transmission informs design and interpretation of field studies and aids development of management strategies for oyster aquaculture.

The impact of aid on health outcomes in Uganda.

The health sector has attracted significant foreign aid; however, evidence on the effectiveness of this support is mixed. This paper combines household panel data with geographically referenced subnational foreign aid data to investigate the contribution of health aid to health outcomes in Uganda. Using a difference-in-differences approach, we find that aid had a strong effect on reducing the productivity burden of disease indicated by days of productivity lost due to illness but was less effective in reducing disease prevalence. Consequently, health aid appeared to primarily quicken recovery times rather than prevent disease. In addition, we find that health aid was most beneficial to individuals who lived closest to aid projects. Apart from the impact of aid, we find that aid tended to not be targeted to localities with the worse socioeconomic conditions. Overall, the results highlight the importance of allocating aid close to subnational areas with greater need to enhance aid effectiveness.

Taking the health aid debate to the subnational level: the impact and allocation of foreign health aid in Malawi.

OBJECTIVE: Cross-national studies provide inconclusive results as to the effectiveness of foreign health aid. We highlight a novel application of using subnational data to evaluate aid impacts, using Malawi as a case study. DESIGN: We employ two rounds of nationally representative household surveys (2004/2005 and 2010/2011) and geo-referenced foreign aid data. We examine the determinants of Malawi's traditional authorities receiving aid according to health, environmental risk, socioeconomic and political factors. We use two approaches to estimate the impact of aid on reducing malaria prevalence and increasing healthcare quality: difference-in-difference models, which include traditional authority and month-of-interview fixed effects and control for individual and household level time-varying factors, and entropy balancing, where models balance on health-related and socioeconomic baseline characteristics. General health aid and four specific health aid sectors are examined. RESULTS: Traditional authorities with greater proportions of individuals living in urban areas, more health facilities and greater proportions of those in major ethnic groups were more likely to receive aid. Difference-in-difference models show health infrastructure and parasitic disease control aid reduced malaria prevalence by 1.20 (95% CI -0.36 to 2.76) and 2.20 (95% CI 0.43 to 3.96) percentage points, respectively, and increased the likelihood of individuals reporting healthcare as more than adequate by 12.1 (95% CI 1.51 to 22.68) and 14.0 (95% CI 0.11 to 28.11) percentage points. Entropy balancing shows similar results. CONCLUSIONS: Aid was targeted to areas with greater existing health infrastructure rather than areas most in need, but still effectively reduced malaria prevalence and enhanced self-reported healthcare quality.