Health Facilities Allocation for COVID-19

AI COMMONS HEALTH & WELLBEING HACKATHON SOLUTIONS

OVERVIEW

The AI Commons Project is a proof of concept of a new methodology of developing Artificial Intelligence solutions that allows anyone, anywhere to benefit from the possibilities that AI can provide. The project aims to increase/improve the accessibility, reproducibility, contextualization and enhancement of Artificial Intelligence solutions globally and especially in emerging markets.

The project aims to demonstrate how a global community of AI experts can learn and co-create mutually beneficial solutions with the opportunity for cross-county incremental enhancement.

STATEMENT OF PURPOSE

INTRODUCTION

Introduce yourself (Documentation team)
Name(s) and institution/organization affiliated with

Data Science Nigeria

Introduce yourself (Solution owners)
Name(s) and institution/organization affiliated with

Aboze Brain John & Rising Odegua (Data Science Nigeria)

Other contributors to this work

PROBLEM DEFINITION

Define the problem
What problem does the solution seek to solve? For how long has the problem existed? How prevalent is problem? Provide any background information relevant to the problem.

In Nigeria, resources allocation in the health sector hasn’t been efficient leading under-utlizing and over utilizing of scarce resources. Due to the COVID-19 pandemic that has been existence since the fall of 2019, it is pertinent for devise an strategic logic to efficiently locate health centers that need to be properly equipped in order to optimally test, detect and isolate the COVID-19 outbreak.

Who are those affected by the problem (Problem owners)
Class of people. Country/region where they live. Impact of problem on their lifestyle

This problem is faced by individuals living in Nigeria cutting across government, industries, and businesses.

How is the problem currently solved?
Describe the current/existing workaround for the problem

Isolation centers are randomly allocated and health centers that are not isolation centers remain ill equipped.

No.

SOLUTION

What is this solution about?
Describe the solution. Describe the solution space and opportunity. When was the solution first released? When was the last release? Link to the solution Deck/publications

The solution uses the factors of social vulnerability index(SVI) such as socioeconomic status, household composition, literacy status and housing and transportation to optimally locate health facilities in Lagos, Nigeria that needs to be revamped/re-equiped during the covid-19 to curtail the spread of the virus. The solution was released in 2020.
Publication: Research Gate

Describe the outputs of the solution
Please ensure that your solution output sufficiently address the problem defined in the section above

The output of the solution is the identification of geographic locations where health facilities are needed based on low, medium and high vulnerability.

Who are the key stakeholders?

Health practitioners
Government
Patients.

What is the expertise types required to develop this solution?
Please provide all technical expertise required to build solution and their roles in the solution development

Geospatial data analysis and analytics.

What are the challenges faced while developing the solution
Decribe the challenges/obstacles faced

N/A.

What are your recommendations to improve the solution?
Provide a list of recommendations, if any, to improve the solution

N/A.

Have you updated this solution documentation before?
When and how often? What sections have changed? Is the documentation updated every time the solution is retrained or updated?

No.

USAGE

What is the intended use of the solution output?
Briefly describe a simple use-case

For government to make strategic decisions in resource and facilities allocation.

Who are the target users?

Government Officials
Health workers.

What are the key procedures followed while using the solution?
How is the input provided? By whom? How is the output returned?

The solution has already identified the geographic locations where health facilities are needed. To update the solution, more dataset that make up the SVI are required, the SVI data then serves as the input loaded in the Geographic Information System (GIS) for geospatial analysis.

Any other comments?

The solution can be made to read user’s incoming text automatically and return a notification appropriately.

DOMAIN AND APPLICATIONS

What are the domains and applications the solution was tested on or used for?
Were domain experts involved in the development, testing, and deployment? Please elaborate

The solution was tested in Lagos State, Nigeria. The covid-19 pandemic is highly correlated to social distancing, and Lagos being the most populous state in Nigeria was an ideal choice.

List applications that the solution has been used for in the past
Please provide information about these applications or relevant pointers. Please provide key performance results for those applications.

None.

DATASET

Describe the dataset
Please provide a brief description of the dataset

The dataset was formulated from percentages of 24 components with weighted distribution via their respective population across the various LGAs. The independent variables were normalized using min-max scaling technique to range of 0-1. The variables used for the construction of the social vulnerability index (SVI) was derived from four(4) domains; socioeconomic status, household composition,literacy status, and housing & transportation.

For what purpose was the dataset created?
Was there a specific task in mind? Was there a specific gap that needed to be filled? Please provide a description

The dataset was created for the purpose of this solution. The compilation of the 24 different datasets was crucial the construction of the Social Vulnerable Index (SVI).

Who created this dataset (e.g., which team, research group) and on behalf of which entity (e.g., company, institution, organization)?

The research team created the dataset.

Who funded the creation of the dataset?
If there is an associated grant, please provide the name of the grantor and the grant name and number

N/A.

COMPOSITION

What do the instances that comprise the dataset represent (e.g., documents, photos, people, countries)?
Are there multiple types of instances (e.g., movies, users, and ratings; people and interactions between them; nodes and edges)? Please provide a description.

JSON, CSV amd Shape files.

How many instances are there in total (of each type, if appropriate)?

Aggregation of 37 instances.

Does the dataset contain all possible instances or is it a sample (not necessarily random) of instances from a larger set?
If the dataset is a sample, then what is the larger set? Is the sample representative of the larger set (e.g., geographic coverage)? If so, please describe how this representativeness was validated/verified. If it is not representative of the larger set, please describe why not (e.g., to cover a more diverse range of instances, because instances were withheld or unavailable).

The dataset is a representative of Nigeria (larger dataset) as it cites Lagos state (Sample) as a case study.

What data does each instance consist of? “Raw” data (e.g., unprocessed text or images)or features?
In either case, please provide a description

Each instance of the dataset consist of percentages of the components that make up the Social Vulnerable Index (SVI).

Is there a label or target associated with each instance?
If so, please provide a description.

No.

Is any information missing from individual instances?
If so, please provide a description, explaining why this information is missing (e.g., because it was unavailable). This does not include intentionally removed information, but might include, e.g., redacted text.

No.

Are relationships between individual instances made explicit (e.g., users’ movie ratings, social network links)?
If so, please describe how these relationships are made explicit.

No.

Are there recommended data splits (e.g., training, development/validation, testing)?
If so, please provide a description of these splits, explaining the rationale behind them

No.

Are there any errors, sources of noise, or redundancies in the dataset?
If so, please provide a description.

No.

Is the dataset self-contained, or does it link to or otherwise rely on external resources (e.g., websites, tweets, other datasets)?
If it links to or relies on external resources, a) are there guarantees that they will exist, and remain constant, over time; b) are there official archival versions of the complete dataset (i.e., including the external resources as they existed at the time the dataset was created); c) are there any restrictions (e.g., licenses, fees) associated with any of the external resources that might apply to a future user? Please provide descriptions of all external resources and any restrictions associated with them, as well as links or other access points, as appropriate

The dataset is reliant of Lagos Bureau of Statistics Poverty Profile and Grid3Global Lagos Health Care Facilities data. All the datasets are open source.

Does the dataset contain data that might be considered confidential (e.g., data that is protected by legal privilege or by doctorpatient confidentiality, data that includes the content of individuals non-public communications)? If so, please provide a description

No.

Does the dataset contain data that, if viewed directly, might be offensive, insulting, threatening, or might otherwise cause anxiety?
If so, please describe why.

No.

Does the dataset relate to people?
If not, you may skip the remaining questions in this section.

Yes.

Does the dataset identify any subpopulations (e.g., by age, gender)?
If so, please describe how these subpopulations are identified and provide a description of their respective distributions within the dataset

The subpopulations were distributed based on socioeconomic status, household composition,literacy status, and housing & transportation.

Is it possible to identify individuals (i.e., one or more natural persons), either directly or indirectly (i.e., in combination with other data) from the dataset?
If so, please describe how.

No, the datasets were aggregated in groups based on the distributions.

Does the dataset contain data that might be considered sensitive in any way (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history)?
If so, please provide a description

No.

COLLECTION PROCESS

How was the data associated with each instance acquired?
Was the data directly observable (e.g., raw text, movie ratings), reported by subjects (e.g., survey responses), or indirectly inferred/derived from other data (e.g., part-of-speech tags, model-based guesses for age or language)? If data was reported by subjects or indirectly inferred/derived from other data, was the data validated/verified? If so, please describe how

The datasets used were derived from Lagos Bureau of Statistics Poverty Profile and Grid3Global Lagos Health Care Facilities.

What mechanisms or procedures were used to collect the data (e.g., hardware apparatus or sensor, manual human curation, software program, software API)?
How were these mechanisms or procedures validated?

The Lagos Bureau of Statistics data was scraped from pdf documents while Grid3Global data is readily downloadable in numerous format online.

If the dataset is a sample from a larger set, what was the sampling strategy (e.g., deterministic, probabilistic with specific sampling probabilities)?

The sampling strategy is deterministic.

Who was involved in the data collection process
(e.g., students, crowdworkers, contractors) and how were they compensated (e.g., how much were crowdworkers paid)?

The Research team.

Over what timeframe was the data collected?
Does this timeframe match the creation timeframe of the data associated with the instances (e.g., recent crawl of old news articles)? If not, please describe the timeframe in which the data associated with the instances was created.

Less than a month.

Were any ethical review processes conducted
(e.g., by an institutional review board)? If so, please provide a description of these reviewprocesses, including the outcomes, as well as a link or other access pointto any supporting documentation.

The ethical review on the work were carried by Data Science Nigeria’s Research Lead and Technical Delivery Lead.

Does the dataset relate to people? If not, you may skip the remainder of the questions in this section.

Yes.

Did you collect the data from the individuals in question directly, or obtain it via third parties or other sources (e.g., websites)?

Third-parties sourced.

Were the individuals in question notified about the data collection? If so, please describe (or show with screenshots or other information) how notice was provided, and provide a link or other access point to, or otherwise reproduce, the exact language of the notification itself.

N/A.

Did the individuals in question consent to the collection and use of their data? If so, please describe (or show with screenshots or other information) how consent was requested and provided, and provide a link or other access point to, or otherwise reproduce, the exact language to which the individuals consented.

N/A.

If consent was obtained, were the consenting individuals provided with a mechanism to revoke their consent in the future or for certain uses?
If so, please provide a description, as well as a link or other access point to the mechanism (if appropriate).

N/A.

Has an analysis of the potential impact of the dataset and its use on data subjects
(e.g., a data protection impact analysis)been conducted? If so, please provide a description of this analysis, including the outcomes, as well as a link or other access point to any supporting documentation.

N/A.

PREPROCESSING/CLEANING/
LABELLING

Was any preprocessing/cleaning/labeling of the data done (e.g., discretization or bucketing, tokenization, part-of-speech tagging, SIFT feature extraction, removal of instances, processing of missing values)?
If so, please provide a description. If not, you may skip the remainder of the questions in this section.

Exploaratory Data Analysis, dropping redundant features and standardization of the datasets.

Was the “raw” data saved in addition to the preprocessed/cleaned/labeled data (e.g., to support unanticipated future uses)?
If so, please provide a link or other access point to the “raw” data.

No.

Is the software used to preprocess/clean/label the instances available?
If so, please provide a link or other access point.

Python (which is open sourced).

USES

Has the dataset been used for any tasks already?
If so, please provide a description.

No.

Is there a repository that links to any or all papers or systems that use the dataset?
If so, please provide a link or other access point

No.

What (other) tasks could the dataset be used for?

Further research on the subject matter.

Is there anything about the composition of the dataset or the way it was collected and preprocessed/cleaned/labeled that might impact future uses?
For example, is there anything that a future user might need to know to avoid uses that could result in unfair treatment of individuals or groups (e.g., stereotyping, quality of service issues) or other undesirable harms (e.g., financial harms, legal risks) If so, please provide a description. Is there anything a future user could do to mitigate these undesirable harms?

No.

Are there tasks for which the dataset should not be used?
If so, please provide a description.

No.

MAINTENANCE

Who is supporting/hosting/maintaining the dataset?

Data Science Nigeria

How can the owner/curator/manager of the dataset be contacted (e.g., email address)?

A message can be sent by filling the form HERE

Is there an erratum?
If so, please provide a link or other access point.

No.

Will the dataset be updated (e.g., to correct labeling errors, add new instances, delete instances)?
If so, please describe how often, by whom, and how updates will be communicated to users (e.g., mailing list, GitHub)?

The datasets update schedule is depended on third-parties(Lagos Bureau of Statistics & Grid3Global) updates.

If the dataset relates to people, are there applicable limits on the retention of the data associated with the instances (e.g., were individuals in question told that their data would be retained for a fixed period of time and then deleted)?
If so, please describe these limits and explain how they will be enforced.

N/A.

Will older versions of the dataset continue to be supported/hosted/maintained?
If so, please describe how. If not, please describe how its obsolescence will be communicated to users.

N/A.

If others want to extend/augment/build on/contribute to the dataset, is there a mechanism for them to do so?
If so, please provide a description. Will these contributions be validated/verified? If so, please describe how. If not, why not? Is there a process for communicating/distributing these contributions to other users? If so, please provide a description.

N/A.

DATASET PUBLICLY AVAILABLE

List all the datasets?
For each of the dataset used, please provide a brief description, provide a link to the source of the dataset, attach/provide the link to the datasheet or data statement. If a random sample of the dataset is used, please give description

All dataset used can be found HERE.
The description can be found in GRID3 et MEBP3

Did the solution require any transformation of the data in addition to those provided in the datasheet? If yes, specify which dataset(s) the transformation was done on and describe the transformation

Yes, standardization of the dataset.

Did you use synthetic data? If yes, how was it created? Brieﬂy describe its properties and the creation procedure.

No.

MODEL

MODEL DETAILS

A clear description of the model.
Please provide the model date, model version, model type and Information about training algorithms, parameters, fairness constraints or other applied approaches, and features

N/A.

A clear description of the data preparation for the model.
Please provide details of preprocessing, feature engineering and feature selection.

N/A.

Is a pre-trained model used?
If yes, please give a brief description of the pre-trained model(s)

N/A.

A clear explanation of any assumptions.

N/A.

An analysis of the complexity (time, space, sample size) of algorithm used

N/A.

When were the models last updated?
How much did the performance change with each update? How often are the models retrained or updated?

N/A.

EVALUATION

TESTING THE SOLUTION

Describe the testing methodology
Please provide details on train, test and holdout data. What performance metrics were used? (e.g. accuracy, error rates, AUC, precision/recall). Please briefly justify the choice of metrics.

N/A.

Describe the test results
Were latency, throughput, and availability measured? If yes, briefly include those metrics as well.

N/A.

TESTING BY THIRD PARTIES

Is there a way to verify the performance metrics (e.g., via a service API )?
Briefly describe how a third party could independently verify the performance of the service. Are there benchmarks publicly available and adequate for testing the service

No.

In addition to the solution provider, was this service tested by any third party?
Please list all third parties that performed the testing. Also, please include information about the tests and test results.

No.

RESULT

RESULT DETAILS

A clear description of the model result.
Please include the range of hyper-parameters considered, method to select the best hyper-parameter configuration, and specification of all hyper-parameters used to generate results.

N/A.

The exact number of training and evaluation runs

A clear definition of the specific measure or statistics used to report results.

N/A.

A description of results with central tendency (e.g. mean) & variation (e.g. error bars)

N/A.

The average runtime for each result, or estimated energy cost

N/A.

ENVIRONMENT

A description of the computing infrastructure used.
Please include the operating system of the machine, the programming language version, all depedencies and their version

We used Windows 10 on 64 bit. Data analysis and wrangling were carried out by Python 3.7 and Esri ArcGIS Pro.

Is the solution deployed?
If yes, on what platform is it deployed. What tools are used?

No.

STEPS TO REPRODUCE THE SOLUTION

RESULT DETAILS

A clear description of steps to reproduce the solution
Link to the solution on a hosting service such as Kaggle Kernels, Google Colaboratory, etc. If you did not use a hosting service, please try to setup the project from scratch on a new computer and writing down each step as you do it. Please, try to minimize the number of steps to reproduce. For example, move as much code as possible into scripts that can be batch called from a notebook or include a single script that acquires the data, prepares the environment, and executes the code with a single command. If you used a container or virtual machine(VM) , please include instructions on how to download and set up docker or VM. If you did not use a hosting service/docker/VM, please confirm that you have correctly provided all dependencies with their versions and subversions in the "Environment" section

To reproduce this work:
Download the solution files form HERE
Download the shape file of Lagos state local government administrative boundaries from HERE Navigate to Notebooks and open COVID19_note.ipynb
Run all the cells in the notebook to generate the Social Vulunerability Index(SVI) dataset
The generated dataset is then overlayed on Lagos state shape map using ArcGIS.

SAFETY

GENERAL

Are you aware of possible examples of bias, ethical issues, or other safety risks as a result of using the solution?
Were the possible sources of bias or unfairness analyzed? Where do they arise from: the data? the particular techniques being implemented? other sources? Is there any mechanism for redress if individuals are negatively affected?

The possible sources of bias/ unfairness wasn’t analyzed.

Do you use data from or make inferences about individuals or groups of individuals. Have you obtained their consent?
How was it decided whose data to use or about whom to make inferences? Do these individuals know that their data is being used or that inferences are being made about them? What were they told? When were they made aware? What kind of consent was needed from them? What were the procedures for gathering consent? Please attach the consent form to this declaration. What are the potential risks to these individuals or groups? Might the service output interfere with individual rights? How are these risks being handled or minimized? What trade-offs were made between the rights of these individuals and business interests? Do they have the option to withdraw their data? Can they opt out from inferences being made about them? What is the withdrawal procedure?

N/A.

EXPLAINABILITY

Are the solution outputs explainable and/or interpretable?
Please explain how explainability is achieved (e.g. directly explainable algorithm, local explainability, explanations via examples). Who is the target user of the explanation (ML expert, domain expert, general consumer, etc.) Please describe any human validation of the explainability of the algorithms

The solution is made explainable visually via maps.

FAIRNESS

Does the solution implement and perform any bias detection and remediation?
Please describe model bias policies that were checked, bias checking methods, and results (e.g., disparate error rates across different groups). What procedures were used to perform the remediation? Please provide links or references to corresponding technical papers. Please provide details about the value of any bias estimates before and after such remediation. How did the value of other performance metrics change as a result?

No.

CONCEPT DRIFT

What is the expected performance on unseen data or data with different distributions?
Please describe any relevant testing done along with test results.

The solution was not tested on different unseen data apart from the one used to develop the solution.

Does your solution make updates to its behavior based on newly ingested data?
Is the new data uploaded by users? Is it generated by an automated process? Are the patterns in the data largely static or do they change over time? Are there any performance guarantees/bounds? Does the service have an automatic feedback/retraining loop, or is there a human in the loop?

No.

How is the solution tested and monitored for model or performance drift over time?
If applicable, describe any relevant testing along with test results.

Yes, this can be achieved by carrying out the analysis and mapping again. This is highly dependent on updates of the third party bodies in charge of the datasets.

How can the solution be checked for correct, expected output when new data is added?

Correctness can be analyzed from GIS software reports and through visual inspection of the output maps.

Does the solution allow for checking for differences between training and usage data?
Does it deploy mechanisms to alert the user of the difference?

N/A.

Do you test the solution periodically?
Does the testing includes bias or fairness related aspects? How has the value of the tested metrics evolved over time?

N/A.