Software Architecture

Introduction

About RUSH

The Kenya Rural Urban Sanitation and Hygiene (RUSH) platform is an advanced and comprehensive real-time monitoring and information system owned by the Ministry of Health in Kenya. This platform is designed to streamline and enhance the management of sanitation and hygiene data at both county and national levels.

One of the notable capabilities of the RUSH platform is its ability to handle large amounts of data efficiently. It supports Excel bulk upload, allowing users to upload data in bulk from Excel spreadsheets, which can significantly expedite the data entry process. Additionally, the platform features a web-form batch submission functionality, enabling users to submit multiple data entries through a user-friendly web-based interface.

To ensure data accuracy and reliability, the RUSH platform incorporates a data review and approval hierarchy between administrative levels. This means that data entered into the system undergoes a rigorous review process, where it is checked and approved by designated personnel at various administrative levels. This hierarchical approach ensures that data is thoroughly reviewed and validated before being utilized for analysis and decision-making.

Another significant aspect of the RUSH platform is its visualization capabilities. The platform follows the Joint Monitoring Programme (JMP) standard and the RUSH (Rural Urban Sanitation) standard when presenting data visually. By adhering to these standards, the platform ensures consistency and comparability in data visualization across different geographical areas and time periods. The visualizations generated by the platform help in understanding trends, patterns, and gaps in sanitation and hygiene metrics, providing valuable insights for policymakers, stakeholders, and researchers.

The purpose of RUSH Platform

The purpose of the Kenya Rural Urban Sanitation and Hygiene (RUSH) platform is to support effective monitoring, management, and improvement of sanitation and hygiene practices in Kenya. It serves as a comprehensive information system owned by the Ministry of Health, aiming to address the challenges and gaps in sanitation and hygiene by providing reliable data, analysis, and visualization tools.

1. Data Collection and Aggregation: The RUSH platform serves as a centralized repository for collecting and aggregating both quantitative and qualitative data related to sanitation and hygiene practices. It allows for data collection at the county and national levels, ensuring comprehensive coverage and representation of diverse geographical areas.
2. Real-Time Monitoring: The platform operates in real-time, enabling timely monitoring of sanitation and hygiene indicators. This real-time monitoring helps identify emerging trends, gaps, and challenges, allowing for prompt intervention and decision-making.
3. Data Analysis and Insights: The RUSH platform facilitates data analysis, allowing policymakers and stakeholders to gain valuable insights into the state of sanitation and hygiene practices across different regions and demographics. By analyzing the collected data, trends, patterns, and areas of improvement can be identified, contributing to evidence-based decision-making and targeted interventions.
4. Reporting and Visualization: The platform enables the generation of reports and visualizations based on the collected data. The reports provide a comprehensive overview of the sanitation and hygiene situation, highlighting key indicators, challenges, and progress. The visualizations, following the JMP and RUSH standards, make complex data easily understandable, aiding in communication and knowledge dissemination.
5. Decision Support: The RUSH platform acts as a decision support system, providing policymakers, health officials, and other stakeholders with the necessary information to formulate policies, design interventions, and allocate resources effectively. The data-driven insights and visualizations empower decision-makers to prioritize areas for improvement, target resources where they are most needed, and track progress over time.
6. Collaboration and Accountability: The platform enhances collaboration between different administrative levels and stakeholders involved in sanitation and hygiene management. It establishes a data review and approval hierarchy, ensuring the accuracy and reliability of data. By promoting transparency and accountability, the platform facilitates coordinated efforts towards achieving national and international targets related to sanitation and hygiene.
7. Continuous Improvement: The RUSH platform can be continually updated and enhanced to align with evolving needs and priorities. As new data sources, indicators, or best practices emerge, the platform can be adapted to incorporate these changes, ensuring that it remains a relevant and effective tool for monitoring and managing sanitation and hygiene in Kenya.

By leveraging technology and real-time data, the platform aims to contribute to better health outcomes, improved living conditions, and sustainable development in both rural and urban areas of the country.

Functional Overview

The platform offers an array of functionalities that streamline data management and analysis. It supports Excel bulk upload, enabling rapid and efficient data entry from Excel spreadsheets. Additionally, the web-form batch submission feature provides a user-friendly interface for submitting multiple data entries, simplifying the data collection process.

To ensure data accuracy and reliability, the RUSH platform incorporates a robust data review and approval hierarchy between administrative levels. This hierarchical approach guarantees that data is thoroughly reviewed, validated, and approved by designated personnel, enhancing the credibility and quality of the information within the system.

The RUSH platform promotes collaboration and accountability by fostering engagement between different administrative levels and stakeholders involved in sanitation and hygiene management. It acts as a decision support system, providing policymakers and health officials with the necessary information to formulate policies, design interventions, and allocate resources effectively. Additionally, the platform encourages continuous improvement by being adaptable to changing needs and priorities, accommodating new data sources, indicators, and best practices.

the RUSH platform's functional overview highlights its role as a comprehensive system for data collection, analysis, reporting, and visualization.

...

Design Considerations

The design of the RUSH platform incorporates several key considerations to ensure its effectiveness in addressing the challenges and requirements of managing sanitation and hygiene practices in Kenya. Some of the design considerations of the RUSH platform include:

1. Data Aggregation and Integration: The RUSH platform is designed to aggregate both quantitative and qualitative data from various sources and administrative levels. It integrates data from county and national levels, allowing for comprehensive and unified data management. This design consideration enables a holistic view of sanitation and hygiene practices across different geographical areas.
2. Real-time Monitoring and Reporting: The platform emphasizes real-time monitoring of sanitation and hygiene indicators. It provides timely updates on data collection, analysis, and reporting, enabling prompt interventions and decision-making. This design consideration ensures that stakeholders have access to the most up-to-date information to address emerging challenges effectively.
3. User-Friendly Interface: The RUSH platform features a user-friendly interface that enhances usability and accessibility. It is designed with intuitive navigation, clear visual cues, and streamlined workflows. This consideration enables users of varying technical backgrounds to easily navigate the platform and perform tasks efficiently.
4. Role-Based Access and Permissions: The platform employs role-based access control, assigning different levels of access and permissions based on user roles and administrative levels. This design consideration ensures data security, privacy, and appropriate data management by allowing users to access only the functionalities and data relevant to their roles and responsibilities.
5. Data Validation and Approval Hierarchy: The RUSH platform incorporates a data validation process and approval hierarchy to ensure data accuracy and reliability. Appropriate users at different administrative levels review, validate, and approve the data, maintaining data integrity throughout the platform.
6. Standardized Visualizations: The platform follows standardized visualization practices, including the Joint Monitoring Programme (JMP) standard and the RUSH standard. This design consideration ensures consistency and comparability in data visualizations, allowing for meaningful insights and effective communication of information across different regions and time periods.
7. Scalability and Adaptability: The design of the RUSH platform takes into account its scalability and adaptability. It is built to accommodate a growing volume of data and changing requirements over time. This consideration ensures that the platform can evolve and meet the changing needs of sanitation and hygiene management in Kenya.

Integration of Existing Systems

Systems:

The design of the RUSH platform takes into consideration the integration of existing systems and data sources. It aims to leverage and integrate with other relevant platforms, databases, and information systems to facilitate data exchange, interoperability, and collaboration.

These design considerations are aimed at creating a robust, user-friendly, and scalable platform that effectively supports data management, analysis, reporting, and decision-making for improved sanitation and hygiene practices in Kenya.

Architecture

User Roles

The RUSH platform offers a range of user roles, each with its own set of capabilities and responsibilities. The Super Admin holds the highest level of administrative authority at the national level and oversees the overall operation of the platform. County Admins have the responsibility of managing the platform within their respective counties, while Data Approvers review and approve data at the sub-county level. Data Entry Staff are responsible for collecting data at the ward level, ensuring that information is captured accurately at the grassroots level. Additionally, Institutional Users have access to view and download data from all counties, facilitating research and analysis.

These user roles, aligned with administrative levels, contribute to the effective management of sanitation and hygiene data. By assigning specific roles and access privileges, the RUSH platform ensures that data is collected, validated, and utilized appropriately. This promotes accountability, collaboration, and evidence-based decision-making, leading to improved sanitation and hygiene practices throughout Kenya.

In the following sections is the detailed descriptions of each user role, outlining their specific capabilities, page access, administration levels, and responsibilities. Understanding the functions and responsibilities of these user roles is vital to effectively utilizing the RUSH platform and harnessing its full potential for transforming sanitation and hygiene practices in Kenya.

1. Super Admin: The Super Admin holds the highest level of administrative authority in the RUSH platform at the national level. They have access to all functionalities and pages, including user management, data control, visualization, questionnaires, approvals, and reports. As the overall national administrator, their responsibilities encompass assigning roles to County Admins, managing the organization's settings, and overseeing the platform's operations. The Super Admin plays a crucial role in ensuring the smooth functioning and effective utilization of the RUSH platform nationwide.
2. County Admin: County Admins are responsible for overseeing the RUSH platform at the county level. They possess extensive access to functionalities and pages, including user management, data control, visualization, questionnaires, approvals, and reports. Their primary role involves managing and coordinating the platform's operations within their respective counties. This includes assigning roles to Sub County RUSH Admins (Approvers) operating at the sub-county level, who play a crucial role in data management and approval. County Admins act as key facilitators in ensuring efficient and accurate data collection and analysis within their counties.
3. Data Approver: Data Approvers hold the responsibility of giving final approval to the data submitted from their respective sub-counties. Operating at the sub-county administrative level, they possess access to functionalities and pages such as data control, visualization, approvals, questionnaires, and reports. Data Approvers play a critical role in reviewing and validating data submitted by Data Entry Staff from their areas of jurisdiction. They have the authority to edit or return data for correction, ensuring data accuracy and reliability within their assigned sub-counties.
4. Data Entry Staff: Data Entry Staff operate at the ward administrative level and are responsible for collecting data from the communities or villages assigned to them. They have access to functionalities and pages related to data entry, form submissions, data control, visualization, and reports. Data Entry Staff play an essential role in gathering accurate and comprehensive data at the grassroots level, ensuring that the RUSH platform captures information directly from the targeted areas. Their diligent data collection efforts contribute to the overall effectiveness and reliability of the sanitation and hygiene data within the platform.
5. Institutional User: Institutional Users have access to functionalities and pages such as profile management, visualization, and reports. They can view and download data from all counties within the RUSH platform. Institutional Users do not possess administrative privileges but play a vital role in accessing and utilizing the data for research, analysis, and decision-making purposes. Their ability to access data from multiple administrative levels ensures comprehensive insights and contributes to informed actions and interventions in the field of sanitation and hygiene.

Administrative Levels

The administrative levels within the RUSH platform are of utmost importance as they serve as a fundamental backbone for various components within the system. These administrative levels, provided by the Ministry of Health, play a crucial role in user management, data organization, and the establishment of approval hierarchy rules. As such, this master list of administrative levels stands as a critical component that needs to be accurately provided by the Ministry of Health.

The administrative levels serve as a key reference for assigning roles and access privileges to users. Users are associated with specific administrative levels based on their responsibilities and jurisdiction. The administrative levels determine the data organization structure, allowing for effective data aggregation, review, and approval processes. The approval hierarchy rules are established based on these administrative levels, ensuring proper authorization and validation of submitted data. Additionally this allows for effective data visualization, filtering, and analysis based on administrative boundaries. 

The administrative levels consist of distinct administrative names, level names, and unique identifiers, allowing for easy identification and filtering of data points within the platform.

1. National: The National level represents the highest administrative level within the RUSH platform. It encompasses the entire country of Kenya and serves as the top-level jurisdiction for data management, coordination, and decision-making.
2. County: The County level represents the second administrative level within the RUSH platform. It corresponds to the various counties in Kenya and acts as a primary jurisdiction for data collection, management, and implementation of sanitation and hygiene initiatives.
3. Sub-County: The Sub-County level represents the third administrative level within the RUSH platform. It corresponds to the sub-county divisions within each county and serves as a localized jurisdiction for data collection, review, and approval processes.
4. Ward: The Ward level represents the fourth administrative level within the RUSH platform. It corresponds to the wards or smaller subdivisions within each sub-county. Wards act as the grassroots level of data collection, ensuring that data is collected at the most localized and community-specific level.

...

Class Diagrams

Sequence Diagrams

Data Flow Diagrams

...Replace with draw.io

Database Schema

Table Overview

pos	table	column	null	dtype	len	default
1	access	id	NO	bigint		access_id_seq
2	access	role	NO	int
3	access	administration_id	NO	bigint
4	access	user_id	NO	bigint

pos	table	column	null	dtype	len	default
1	administrator	id	NO	bigint		administrator_id_seq
2	administrator	code	YES	character varying	255
3	administrator	name	NO	text
4	administrator	level_id	NO	bigint
5	administrator	parent_id	YES	bigint
6	administrator	path	YES	text

pos	table	column	null	dtype	len	default
1	answer	id	NO	bigint		answer_id_seq
2	answer	name	YES	text
3	answer	value	YES	double
4	answer	options	YES	jsonb
5	answer	created	NO	tz timestamp
6	answer	updated	YES	tz timestamp
7	answer	created_by_id	NO	bigint
8	answer	data_id	NO	bigint
9	answer	question_id	NO	bigint

pos	table	column	null	dtype	len	default
1	answer_history	id	NO	bigint		answer_history_id_seq
2	answer_history	name	YES	text
3	answer_history	value	YES	double
4	answer_history	options	YES	jsonb
5	answer_history	created	NO	tz timestamp
6	answer_history	updated	YES	tz timestamp
7	answer_history	created_by_id	NO	bigint
8	answer_history	data_id	NO	bigint
9	answer_history	question_id	NO	bigint

pos	table	column	null	dtype	len	default
1	batch	id	NO	bigint		batch_id_seq
2	batch	name	NO	text
3	batch	uuid	YES	uuid
4	batch	file	YES	character varying	200
5	batch	created	NO	tz timestamp
6	batch	updated	YES	tz timestamp
7	batch	administration_id	NO	bigint
8	batch	form_id	NO	bigint
9	batch	user_id	NO	bigint
10	batch	approved	NO	bool

pos	table	column	null	dtype	len	default
1	batch_comment	id	NO	bigint		batch_comment_id_seq
2	batch_comment	comment	NO	text
3	batch_comment	created	NO	tz timestamp
4	batch_comment	batch_id	NO	bigint
5	batch_comment	user_id	NO	bigint

pos	table	column	null	dtype	len	default
1	data	id	NO	bigint		data_id_seq
2	data	name	NO	text
3	data	geo	YES	jsonb
4	data	created	NO	tz timestamp
5	data	updated	YES	tz timestamp
6	data	administration_id	NO	bigint
7	data	created_by_id	NO	bigint
8	data	form_id	NO	bigint
9	data	updated_by_id	YES	bigint

pos	table	column	null	dtype	len	default
1	form	id	NO	bigint		form_id_seq
2	form	name	NO	text
3	form	version	NO	int
4	form	uuid	NO	uuid
5	form	type	YES	int

pos	table	column	null	dtype	len	default
1	form_approval_assignment	id	NO	bigint		form_approval_assignment_id_seq
2	form_approval_assignment	updated	YES	tz timestamp
3	form_approval_assignment	administration_id	NO	bigint
4	form_approval_assignment	form_id	NO	bigint
5	form_approval_assignment	user_id	NO	bigint

pos	table	column	null	dtype	len	default
1	form_approval_rule	id	NO	bigint		form_approval_rule_id_seq
2	form_approval_rule	administration_id	NO	bigint
3	form_approval_rule	form_id	NO	bigint

pos	table	column	null	dtype	len	default
1	jobs	id	NO	bigint		jobs_id_seq
2	jobs	type	NO	int
3	jobs	status	NO	int
4	jobs	attempt	NO	int
5	jobs	result	YES	text
6	jobs	info	YES	jsonb
7	jobs	created	NO	tz timestamp
8	jobs	available	YES	tz timestamp
9	jobs	user_id	NO	bigint
10	jobs	task_id	YES	character varying	50

pos	table	column	null	dtype	len	default
1	levels	id	NO	bigint		levels_id_seq
2	levels	name	NO	character varying	50
3	levels	level	NO	int

pos	table	column	null	dtype	len	default
1	option	id	NO	bigint		option_id_seq
2	option	order	YES	bigint
3	option	code	YES	character varying	255
4	option	name	NO	text
5	option	other	NO	bool
6	option	question_id	NO	bigint

pos	table	column	null	dtype	len	default
1	organisation	id	NO	bigint		organisation_id_seq
2	organisation	name	NO	character varying	255

pos	table	column	null	dtype	len	default
1	organisation_attribute	id	NO	bigint		organisation_attribute_id_seq
2	organisation_attribute	type	NO	int
3	organisation_attribute	organisation_id	NO	bigint

pos	table	column	null	dtype	len	default
1	pending_answer	id	NO	bigint		pending_answer_id_seq
2	pending_answer	name	YES	text
3	pending_answer	value	YES	double
4	pending_answer	options	YES	jsonb
5	pending_answer	created	NO	tz timestamp
6	pending_answer	updated	YES	tz timestamp
7	pending_answer	created_by_id	NO	bigint
8	pending_answer	pending_data_id	NO	bigint
9	pending_answer	question_id	NO	bigint

pos	table	column	null	dtype	len	default
1	pending_answer_history	id	NO	bigint		pending_answer_history_id_seq
2	pending_answer_history	name	YES	text
3	pending_answer_history	value	YES	double
4	pending_answer_history	options	YES	jsonb
5	pending_answer_history	created	NO	tz timestamp
6	pending_answer_history	updated	YES	tz timestamp
7	pending_answer_history	created_by_id	NO	bigint
8	pending_answer_history	pending_data_id	NO	bigint
9	pending_answer_history	question_id	NO	bigint

pos	table	column	null	dtype	len	default
1	pending_data	id	NO	bigint		pending_data_id_seq
2	pending_data	name	NO	text
3	pending_data	geo	YES	jsonb
5	pending_data	created	NO	tz timestamp
6	pending_data	administration_id	NO	bigint
7	pending_data	created_by_id	NO	bigint
8	pending_data	data_id	YES	bigint
9	pending_data	form_id	NO	bigint
11	pending_data	batch_id	YES	bigint
12	pending_data	updated	YES	tz timestamp
13	pending_data	updated_by_id	YES	bigint

pos	table	column	null	dtype	len	default
1	pending_data_approval	id	NO	bigint		pending_data_approval_id_seq
2	pending_data_approval	status	NO	int
4	pending_data_approval	user_id	NO	bigint
5	pending_data_approval	level_id	NO	bigint
6	pending_data_approval	batch_id	NO	bigint

pos	table	column	null	dtype	len	default
1	question	id	NO	bigint		question_id_seq
2	question	order	YES	bigint
3	question	text	NO	text
4	question	name	NO	character varying	255
5	question	type	NO	int
6	question	meta	NO	bool
7	question	required	NO	bool
8	question	rule	YES	jsonb
9	question	dependency	YES	jsonb
10	question	form_id	NO	bigint
11	question	question_group_id	NO	bigint
12	question	api	YES	jsonb
13	question	extra	YES	jsonb

pos	table	column	null	dtype	len	default
1	question_attribute	id	NO	bigint		question_attribute_id_seq
2	question_attribute	name	YES	text
3	question_attribute	attribute	NO	int
4	question_attribute	options	YES	jsonb
5	question_attribute	question_id	NO	bigint

pos	table	column	null	dtype	len	default
1	question_group	id	NO	bigint		question_group_id_seq
2	question_group	name	NO	text
3	question_group	form_id	NO	bigint
4	question_group	order	YES	bigint

pos	table	column	null	dtype	len	default
1	system_user	id	NO	bigint		system_user_id_seq
2	system_user	password	NO	character varying	128
3	system_user	last_login	YES	tz timestamp
4	system_user	is_superuser	NO	bool
5	system_user	email	NO	character varying	254
6	system_user	date_joined	NO	tz timestamp
7	system_user	first_name	NO	character varying	50
8	system_user	last_name	NO	character varying	50
9	system_user	designation	YES	character varying	50
10	system_user	phone_number	YES	character varying	15
11	system_user	updated	YES	tz timestamp
12	system_user	deleted_at	YES	tz timestamp
13	system_user	organisation_id	YES	bigint
14	system_user	trained	NO	bool

pos	table	column	null	dtype	len	default
1	user_form	id	NO	bigint		user_form_id_seq
2	user_form	form_id	NO	bigint
3	user_form	user_id	NO	bigint

Materialized Views

Database Relationship

User Interface Design

Error Handling

Error Handling Rules

The platform incorporates robust error handling strategies to address various types of errors that may occur during operation. Here are the key considerations for error handling in the RUSH platform:

1. Error Logging and Monitoring: The platform logs errors and exceptions that occur during runtime. These logs capture relevant details such as the error type, timestamp, user context, and relevant system information. Error logs enable developers and administrators to identify and troubleshoot issues efficiently, helping to improve system reliability and performance.
2. User-Friendly Error Messages: When errors occur, the platform provides user-friendly error messages that communicate the issue clearly and concisely. Clear error messages help users understand the problem and take appropriate actions or seek assistance. The messages may include relevant details about the error, potential solutions, and contact information for support if necessary.
3. Graceful Degradation and Recovery: The platform is designed to handle errors gracefully, minimizing disruptions and providing fallback mechanisms where possible. For example, if a specific functionality or service becomes temporarily unavailable, the platform can display a fallback message or provide alternative options to ensure users can continue their work or access relevant information.
4. Error Validation and Input Sanitization: The platform applies comprehensive input validation and sanitization techniques to prevent and handle errors caused by invalid or malicious user input. This includes validating user-submitted data, sanitizing inputs to prevent code injection or script attacks, and ensuring that data conforms to expected formats and ranges. Proper input validation reduces the risk of errors and security vulnerabilities.
5. Exception Handling and Error Recovery: The platform utilizes exception handling mechanisms to catch and handle errors gracefully. Exceptions are caught, logged, and processed to prevent system crashes or unexpected behavior. The platform incorporates appropriate error recovery strategies, such as rolling back transactions or reverting to previous states, to maintain data integrity and prevent data loss or corruption.
6. Error Reporting and Support Channels: The platform provides channels for users to report errors and seek support. These channels can include contact forms, dedicated support email addresses, or a help-desk system. By offering reliable channels for error reporting and support, users can report issues promptly and receive assistance in resolving them effectively.
7. Continuous Improvement: The platform regularly assesses error patterns and user feedback to identify recurring issues and areas for improvement. By analyzing error trends, the development team can prioritize bug fixes, optimize system components, and enhance the overall stability and reliability of the platform.

List Errors

...

Security Considerations

The RUSH platform incorporates multiple security measures to safeguard data, protect user privacy, and ensure secure operations across its Docker containers and cloud-based infrastructure. Here are the key security considerations in the platform:

1. Container Security (Docker): The Docker containers, including the Back-end and Worker containers, are designed with security in mind. The containers are configured to follow best practices such as using official base images, regularly updating dependencies, and employing secure container runtime configurations. These measures reduce the risk of vulnerabilities and unauthorized access within the containerized environment.
2. Access Control and Authentication: The platform implements robust access control mechanisms to ensure that only authorized users can access the system and its functionalities. User authentication, such as through the use of JWT (JSON Web Token), is employed to verify user identities and grant appropriate access based on roles and permissions. This helps prevent unauthorized access to sensitive data and functionalities.
3. Network Security (NGINX): The Front-end container, powered by NGINX, helps enforce security measures at the network level. NGINX can be configured to handle SSL/TLS encryption, protecting data in transit between users and the platform. It can also serve as a reverse proxy, effectively managing incoming traffic and providing an additional layer of security to prevent potential attacks.
4. Secure Database Storage (Cloud-SQL): The RUSH platform utilizes Cloud-SQL for secure database storage. Cloud-SQL offers built-in security features, including encryption at rest and transit, role-based access control, and regular security updates. These measures help protect the integrity and confidentiality of the platform's data stored in the Cloud-SQL database.
5. Secure File Storage (Cloud Storage Bucket): The platform leverages Cloud Storage Bucket for secure file storage. Cloud Storage provides robust access controls, including fine-grained permissions, encryption, and auditing capabilities. This ensures that data files, such as uploaded documents, are securely stored and protected from unauthorized access. The endpoints of file should only served by the back-end so it also applies authentication.
   
6. Security Monitoring and Auditing: The platform implements security monitoring and auditing tools to detect and respond to potential incidents. System logs and activity records are regularly reviewed to identify any suspicious activities or breaches. Additionally, periodic security audits are conducted to assess and address potential vulnerabilities proactively.
7. User Education and Awareness: The platform emphasizes user education and awareness regarding security best practices. Users are encouraged to follow strong password policies: Lowercase, Numbers, Special Character, Uppercase Character, No White Space, and Minimum 8 Characters. By promoting user security awareness, the platform strengthens overall security posture.

Performance Considerations

The RUSH platform has several performance considerations, particularly in relation to visualization, excel data download, data upload, and validation. While these functionalities are crucial for effective data management and analysis, they can pose potential performance challenges due to the volume and complexity of the data involved. The platform takes these considerations into account to optimize performance and ensure a smooth user experience. Here are the key performance considerations:

1. Visualization: Visualizations are powerful tools for data analysis and communication. However, generating complex visualizations from large datasets can be computationally intensive and may lead to performance issues. The RUSH platform employs optimization techniques, such as efficient data retrieval, caching, and rendering algorithms, to enhance the speed and responsiveness of visualizations. It strives to strike a balance between visual richness and performance to provide users with meaningful insights without sacrificing usability.
2. Excel Data Download: The ability to download data in Excel format is essential for users to perform in-depth analysis and reporting. However, large datasets or complex queries can result in long download times and increased server load. To mitigate this, the RUSH platform optimizes the data retrieval and export process, employing techniques such as data compression and efficient file generation. It aims to minimize download times and ensure a seamless user experience when exporting data to Excel.
3. Data Upload and Validation: Data upload and validation involve processing and verifying large volumes of data. This process can be time-consuming, particularly when dealing with extensive datasets or complex validation rules. The RUSH platform optimizes data upload and validation processes through efficient algorithms and parallel processing techniques. It strives to expedite the data entry process while maintaining data integrity and accuracy.

---

To ensure optimal performance, the RUSH platform continuously monitors system performance, identifies bottlenecks, and implements performance optimizations as needed. This may involve infrastructure scaling, database optimizations, query optimizations, and caching strategies. Regular maintenance and updates are conducted to keep the platform running smoothly and efficiently.

It is worth noting that the platform's performance can also be influenced by factors such as network connectivity, hardware capabilities, and user behavior. To mitigate these factors, the RUSH platform provides guidelines and best practices for users to optimize their own data handling processes and network connectivity.

Deployment Strategy

...

Testing Strategy

Testing Framework and Tools

The RUSH platform employs a comprehensive testing strategy to ensure the reliability, functionality, and quality of both its back-end and front-end components. The testing strategy encompasses different levels of testing, including back-end testing with Django Test, front-end testing with Jest, and container network testing with HTTP (bash). Here is an overview of the testing strategy for the RUSH platform:

Back-end Testing with Django Test

• The back-end testing of the RUSH platform is conducted using Django Test, a testing framework provided by Django.
• Django Test enables the creation of test cases and test suites to evaluate the functionality and behavior of the back-end components.
• Back-end testing focuses on unit tests, integration tests, and API tests to validate individual modules, their interactions, and the API endpoints.
• Test cases cover various scenarios, including positive and negative input cases, edge cases, and boundary conditions to ensure robustness and accuracy.

Front-end Testing with Jest

• The front-end testing of the RUSH platform is performed using Jest, a JavaScript testing framework widely used for testing React applications.
  
• Jest facilitates the creation of unit tests, integration tests, and component tests to assess the behavior and functionality of the front-end components.
  
• Front-end testing focuses on validating the UI components, user interactions, and the correctness of the application's logic and state management.
• Test cases cover various scenarios, including rendering components, user actions, and expected outcomes to ensure the desired user experience and functionality.

Container Network Testing with HTTP (bash) WILL BE REPLACED BY SELENIUM-HQ:

• The RUSH platform conducts container network testing using HTTP (bash) to assess the communication and network connectivity between different containers within the Docker environment.
• Container network testing ensures that the back-end, worker, and front-end containers can communicate effectively and exchange data seamlessly.
• Test scenarios involve sending HTTP requests and verifying the responses, ensuring the expected data flow and connectivity between containers.

The testing strategy for the RUSH platform aims to achieve thorough coverage across the back-end, front-end, and container network aspects. It focuses on validating the functionality, data flow, interactions, and network connectivity within the platform. Test cases are designed to cover a wide range of scenarios, including normal operation, edge cases, and potential error conditions.

Hardware Capability Evaluation

In addition to the testing strategies mentioned earlier, the RUSH platform recognizes the importance of stress testing to evaluate the hardware capability and performance under heavy workloads. This specifically applies to resource-intensive tasks such as data validation and data seeding from the Excel bulk data upload feature. Stress testing is conducted to simulate high-demand scenarios and identify potential bottlenecks or performance issues. Here's an explanation of the stress testing approach:

Stress Testing

Data Validation Stress Test

Data Seeding Stress Test

The stress testing process aims to identify any performance bottlenecks, resource limitations, or scalability issues that may arise when the platform is subjected to heavy workloads. By conducting stress tests, the development team can gather valuable insights and make necessary optimizations to ensure that the platform can handle the expected load and perform optimally under stressful conditions.

The stress testing phase is crucial to validate the hardware capability and scalability of the RUSH platform, particularly during resource-intensive tasks like data validation and data seeding from the Excel bulk data upload feature. By proactively identifying and addressing any performance concerns, the platform can provide a reliable and efficient experience for users, even under demanding scenarios.

Assumptions and Constraints

The development and operation of the RUSH platform are subject to certain assumptions and constraints that influence its design and functionality. These assumptions and constraints are important to consider as they provide context and boundaries for the platform's implementation. Here are the key assumptions and constraints of the RUSH platform:

Technical Infrastructure: The RUSH platform assumes access to a reliable technical infrastructure, including servers, networking components, and cloud-based services. It requires sufficient computational resources, storage capacity, and network connectivity to handle the expected user load and data processing requirements.

1. Data Availability and Quality: The platform assumes the availability and quality of data from various sources, including county and national levels. It relies on the assumption that relevant data is collected, validated, and provided by the respective stakeholders. The accuracy, completeness, and timeliness of the data are crucial for effective analysis and decision-making within the platform.
2. Compliance with Regulatory Requirements: The RUSH platform operates under the assumption that it complies with applicable laws, regulations, and data privacy requirements. It is assumed that necessary consent, data usage, and privacy policies are in place to protect user data and comply with legal obligations.
3. User Adoption and Engagement: The platform assumes user adoption and engagement, as its success relies on active participation and utilization by relevant stakeholders. It assumes that users, including data entry staff, data approvers, administrators, and institutional users, will actively use the platform, contribute accurate data, and engage in data analysis and decision-making processes.
4. System Scalability and Performance: The RUSH platform assumes that it can scale and perform adequately to handle increasing user demand and growing data volumes over time. It assumes that the necessary infrastructure and optimizations can be implemented to maintain system performance, responsiveness, and reliability as the user base and data size expand.
5. Collaboration and Data Sharing: The platform assumes a collaborative environment and willingness among stakeholders to share data and insights. It assumes that relevant agencies, organizations, and institutions are willing to collaborate, contribute data, and use the platform's functionalities for informed decision-making and improved sanitation and hygiene practices.
6. Resource Constraints: The development and maintenance of the RUSH platform operate within resource constraints, such as budgetary limitations, time constraints, and availability of skilled personnel. These constraints may impact the scope, timeline, and features of the platform's implementation and ongoing operations.

Dependencies

Software Dependencies

The RUSH platform incorporates various dependencies and frameworks to enable its functionality and deliver a seamless user experience. The following dependencies are essential components used in the development of the platform:

1. Django: The RUSH platform utilizes Django, a high-level Python web framework, to build the back-end infrastructure. Django provides a solid foundation for handling data management, authentication, and implementing business logic.
2. Pandas: The platform relies on Pandas, a powerful data manipulation and analysis library in Python, to handle data processing tasks efficiently. Pandas enables tasks such as data filtering, transformation, and aggregation, enhancing the platform's data management capabilities.
3. React: The front-end of the RUSH platform is developed using React, a popular JavaScript library for building user interfaces. React enables the creation of dynamic and interactive UI components, ensuring a responsive and engaging user experience.
4. Ant Design (antd): The platform utilizes Ant Design, a comprehensive UI library based on React, to design and implement a consistent and visually appealing user interface. Ant Design provides a rich set of customizable and reusable UI components, streamlining the development process.
5. Echarts: Echarts, a powerful charting library, is integrated into the RUSH platform to generate various data visualizations. With Echarts, the platform can display charts, graphs, and other visual representations of data, enabling users to gain insights and make informed decisions.

6. D3: The RUSH platform incorporates D3.js, a JavaScript library for data visualization. D3.js provides a powerful set of tools for creating interactive and customizable data visualizations, including charts, graphs, and other visual representations. By leveraging D3.js, the platform can deliver dynamic and engaging data visualizations to users.

7. Leaflet: The platform incorporates Leaflet, a JavaScript library for interactive maps, to handle geo-spatial data visualization. Leaflet enables the integration of maps, markers, and other geo-spatial features, enhancing the platform's ability to represent and analyze location-based information.
8. Node-sass: Node-sass is a Node.js library that enables the compilation of Sass (Syntactically Awesome Style Sheets) files into CSS. The RUSH platform uses node-sass to process and compile Sass files, allowing for a more efficient and maintainable approach to styling the user interface.

In addition to the previously mentioned dependencies, the RUSH platform relies on the following essential dependencies and libraries to support its functionality and development process:

1. Django Rest Framework (DRF): The RUSH platform utilizes Django Rest Framework, a powerful and flexible toolkit for building Web APIs. DRF simplifies the development of APIs within the platform, providing features such as request/response handling, authentication, serialization, and validation. It enables seamless integration of RESTful API endpoints, allowing for efficient communication between the frontend and backend components.
2. PyJWT: PyJWT is a Python library that enables the implementation of JSON Web Tokens (JWT) for secure user authentication and authorization. The RUSH platform utilizes PyJWT to generate, validate, and manage JWT tokens. JWT tokens play a crucial role in ensuring secure user sessions, granting authorized access to specific functionalities and data within the platform.
3. Sphinx: Sphinx is a documentation generation tool widely used in Python projects. The RUSH platform incorporates Sphinx to generate comprehensive and user-friendly documentation. Sphinx facilitates the creation of structured documentation, including API references, code examples, and user guides. It streamlines the documentation process, making it easier for developers and users to understand and utilize the platform's features and functionalities.

By leveraging these additional dependencies, including Django Rest Framework, PyJWT, and Sphinx, the RUSH platform gains essential support for building robust APIs, implementing secure authentication mechanisms, and generating comprehensive documentation.

 

These dependencies contribute to the platform's overall functionality, security, and user-friendliness, ensuring a well-rounded and effective solution for managing sanitation and hygiene practices in Kenya.

Master Lists

The RUSH platform incorporates several master lists that play a vital role in its functioning and data management. These master lists include the administrative levels, questionnaire definitions, and the shape-file representing accurate administrative boundaries. The administrative levels master list defines the hierarchical structure of Kenya's administrative divisions, facilitating data organization, user roles, and reporting.

Shape-file and Country Administrative Description

An essential master list in the RUSH platform is the shape-file that accurately represents the administrative levels of Kenya. This shape-file serves as a crucial reference for various components within the system, including user management, data management, and visualization. The importance of the shape-file as a master list lies in its ability to provide precise and standardized administrative boundaries, enabling effective data identification, filtering, and visualization. Here's an explanation of the significance of the shape-file in the RUSH platform:

1. Accurate Administrative Boundaries:

   • The shape-file provides accurate and up-to-date administrative boundaries of Kenya, including the national, county, sub-county, and ward levels.
   • These boundaries define the jurisdictional divisions within the country and serve as a fundamental reference for assigning roles, managing data, and generating reports within the platform.
   • The accuracy of administrative boundaries ensures that data and administrative processes align with the established administrative hierarchy in Kenya.

2. Data Identification and Filtering:

   • The shape-file enables efficient data identification and filtering based on administrative boundaries.
   • By associating data points with the corresponding administrative levels, the platform can retrieve and present data specific to a particular county, sub-county, or ward.
   • This functionality allows users to view, analyze, and report on data at different administrative levels, facilitating targeted decision-making and resource allocation.

3. Visualization and Geographic Context:

   • The shape-file serves as the basis for visualizing data on maps within the RUSH platform.
   • By overlaying data on the accurate administrative boundaries provided by the shapefile, users can visualize the distribution of sanitation and hygiene indicators across different regions of Kenya.
   • This geo-spatial visualization enhances understanding, supports data-driven decision-making, and aids in identifying geographic patterns and disparities.

4. Data Consistency and Standardization:

   • The shape-file, being a standardized and authoritative source, ensures consistency and uniformity in defining administrative boundaries across the platform.
   • It provides a reliable reference that aligns with the official administrative divisions recognized by the Ministry of Health and other relevant authorities.
   • The use of a consistent and standardized master list facilitates data aggregation, analysis, and reporting, ensuring reliable and comparable insights.

The shape-file, sourced from the Ministry of Health, acts as a crucial master list within the RUSH platform. It provides accurate administrative boundaries, supports data identification and filtering, enables geo-spatial visualization, and ensures data consistency and standardization. By utilizing the shape-file as the master list, the platform can effectively manage administrative processes, present data in a meaningful geographic context, and contribute to evidence-based decision-making for improved sanitation and hygiene practices throughout Kenya.

Questionnaire Definitions and Form Management

In addition to the administrative levels, the RUSH platform relies on another important master-list that defines the questionnaires used within the system. The questionnaire definition plays a crucial role in capturing the necessary data points and structuring the information collection process. Managing and maintaining the questionnaire forms are essential before seeding them into the system. This section outlines the importance of questionnaire definitions and the process of form management in the RUSH platform.

1. Questionnaire Definitions:

   • Questionnaire definitions define the structure, content, and data points to be collected during data entry.
   • These definitions specify the questions, response options, and any associated validations or skip patterns.
   • Questionnaire definitions determine the type and format of data that can be entered for each question.
   • These definitions ensure consistency and standardization in data collection across the platform.

2. Form Management:

   • Form management involves the creation, customization, and maintenance of the questionnaire forms.
   • Before seeding the forms into the system, it is crucial to ensure their accuracy, completeness, and adherence to data collection standards.
   • Form management includes activities such as form design, validation rules setup, skip logic configuration, and user interface customization.
   • It is important to conduct thorough testing and quality assurance to ensure that the forms function correctly and capture the required data accurately.

3. Form Fixes and Updates:

   • As part of the form management process, it is essential to address any issues or errors identified during testing or from user feedback.
   • Form fixes and updates may involve resolving bugs, improving user interface elements, modifying question wording, or adjusting validation rules.
   • It is crucial to carefully test and validate the fixed forms to ensure that the changes are successfully implemented and do not introduce new issues.

It is important to note that form management is an iterative process that may involve continuous improvements and updates as new requirements, feedback, or changes in data collection standards arise. Regular monitoring and updates to the questionnaire definitions and forms are essential to ensure the platform's effectiveness and adaptability in capturing accurate and relevant data.

Risks and Mitigation Strategies

The development and operation of the RUSH platform come with inherent risks that can impact its effectiveness, security, and usability. Identifying and addressing these risks through appropriate mitigation strategies is essential to ensure the smooth functioning and success of the platform. Here are some key risks associated with the RUSH platform and their corresponding mitigation strategies:

Data Security and Privacy Risks

Risk: Unauthorized access, data breaches, or misuse of sensitive information.
Mitigation: Implement robust security measures, such as encryption, access controls, and regular security updates. Conduct thorough security audits, provide user education on data security best practices, and ensure compliance with data protection regulations.

Technical Risks

Risk: System failures, infrastructure disruptions, or performance bottlenecks.
Mitigation: Employ redundant and scalable infrastructure to minimize single points of failure. Regularly monitor system performance, conduct load testing, and implement disaster recovery plans. Update software and hardware components to address vulnerabilities and ensure optimal performance.

Data Quality Risks

Risk: Inaccurate, incomplete, or unreliable data affecting decision-making processes.
Mitigation: Implement data validation mechanisms, enforce data entry standards, and provide user training on data collection best practices. Conduct regular data quality checks and provide feedback loops to data entry staff for improvement. Collaborate with data providers to improve data accuracy and completeness.

User Adoption and Engagement Risks

Risk: Low user adoption, resistance to change, or lack of engagement with the platform.
Mitigation: Conduct user needs assessments, involve stakeholders in the platform's design and development process, and provide comprehensive user training and support. Highlight the benefits and value of the platform to promote user adoption and engagement. Continuously gather user feedback and iterate on the platform based on user needs and preferences.

Stakeholder Collaboration Risks

Risk: Limited collaboration and data sharing among stakeholders.
Mitigation: Foster strong partnerships with relevant agencies, organizations, and institutions. Promote a culture of collaboration, sharing best practices, and jointly addressing common challenges. Establish clear data sharing agreements and protocols to encourage stakeholder participation and data contribution.

Resource Risks

Risk: Insufficient resources (human, or technical) for platform development and maintenance.
Mitigation: Develop realistic resource plans and secure adequate funding for the platform's implementation and ongoing operation. Optimize resource allocation, prioritize critical features and functionalities, and leverage partnerships to share resources and expertise.

---

Regular risk assessments, monitoring, and proactive risk management practices should be integrated into the platform's lifecycle to identify emerging risks and implement appropriate mitigation strategies. By effectively addressing these risks, the RUSH platform can operate with increased resilience, security, and usability, enabling the successful management of sanitation and hygiene practices in Kenya.

Implementation Plan

Documentation References

Conclusion