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The market for smartphone apps tracking fertility has grown in recent years. These apps brand themselves as empowering their users to reach their reproductive goals, claiming to help achieve a pregnancy more easily than through conventional medical channels. This paper offers the first comprehensive quantification of fertility tracking app users. Using data collected from Google Play Store and Apple App Store, we calculate the most downloaded apps, and the global distribution of use. We use a log-log model fitted on the Google Play Store data to predict the Apple App Store number of installations. Our findings show that 74% of downloads are for just three of 28 apps. The majority of the reviews are left by users in North America, Northern Europe, and Australia; but it is noteworthy that downloads are also widespread in the Global South. Ongoing work aims to investigate the most discussed topics in the reviews.
Researchers at the Network Dynamics and Simulation Science Laboratory have been using a combination of modeling techniques to predict the spread of the Ebola outbreak.
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Farmer decision making for their fields has been boosted by the introduction of daily weather forecasts and technical advice to mobile telephones in selected rural areas in Mali.The systems being tried out by ICRISAT and NGO partners have still to develop to overcome the illiteracy barrier for many potential users but early results from testing, including training on reading SMS messages, are showing
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two-period panel data set. The results show that while yields
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improve welfare outcomes during and after a natural
disaster. Finally, risk-coping strategies, including financial
account ownership, access to off-farm income sources,
and adult children living away from home, were generally
ineffective in mitigating the negative impacts of the floods.
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Measuring Indicators for Resilience Analysis (MIRA)_presented by Julie Ideh_CRS_BRACC resilience learning event_12 Feb 2020
1. Measuring Indicators for Resilience Analysis (MIRA)
Helping Communities Identify Risk and Plan for It
2. Why do we need to measure resilience?
Drought Projections by end of century (Dai 2011)
Our understanding of poverty has changed from
static to dynamic, acknowledging the high levels
of unplanned risks poor households face in
response to a number of shocks
New focus on resilience in programming, looking
at underlying factors that determine the return
to equilibrium after a shock by households and
communities
It became understood that short-term shocks
could have long-term consequences and that we
needed a methodology for measuring resilience
to these shocks
3. What is MIRA? MIRA is a data collection and analysis scheme to measure and predict
resilience among households prone to food insecurity one to two
months ahead of time
Proof of concept by CRS and Cornell University with 580 households in
Chikwawa. Expanded in 2017 to 2200 household in two adjacent
districts in phase II (entire DFAP project area) in Malawi. Also rolled
out in the Grand Sud in Madagascar in July 2018
Data is collected from sentinel households across the UBALE districts
in Malawi and in the HAVELO districts in Madagascar on a monthly
basis using a quick and simple CommCare application to enable
accurate high-frequency results for analysis
4. MIRA Protocol
• Qualitative research phase, involving
development and adaptation of the
survey instruments and embedding data
collection within existing programming
and communities.
• Data collection, baseline followed by
monthly high-frequency follow-ups on
shocks and key indicators.
• Research and analytics, quantifying
households’ resilience for the purpose of
impact evaluation, targeting and
forecasting
• Community Engagement around
identifying uses of the data and
disseminating findings.
MIRA relies on embedded enumerators; who are hired from within each
community and are trained on a smart-phone enabled survey application
5. Resilience Analysis
MIRA provides a real-time record of
experienced shocks over time, linked to
characteristics that may make them
more or less resilient
By measuring how persistent the
experience of a shock is, we can calculate
how resilient a household is
For instance, households in the flood
plain are more resilient to drought but
less resilient to illness.
8. Predictive capacity
MIRA data also allows us to develop a predictive
model using machine learning algorithms, one
predicting the future incidence of food
insecurity
Good predictors include
• location of fields
• proximity to flood plains
• sex of household head
• previous shocks experienced
Tested two different modelling approaches
(LASSO and Random Forest) to predict shocks
11. Trends in Food Insecurity Disaggregated by Ubale Interventions
12. Potential for scaling to other contexts
MIRA can track the incidence of emerging shocks and threats by
simply adding a question to the instrument
• An example of mapping Fall Army Worm in Nsanje is to the left
• Added a flood module to capture the recent effects of cyclone Idai
Sharing of dashboard with decentralized civil protection
committees in the communities surveyed to utilize the data
collected for planning and mitigation activities
Use MIRA for adaptive programming and improved early warning
Planned scale in other southern districts with PROSPER and other
stakeholder
Cornell and CRS are replicating the protocol in Madagascar to
expand proof of concept in different contexts
• Further expansion to other countries and projects possible and level of
effort depends on data use plan