📌 Data is collected for the Omdena's Local Chapter project- Creating a Rice Disease Classifier using Open Source Data and Computer Vision

All the images are stored in 224 x 224 pixels form for the ease of training the models.

1. BACKGROUND - Rice is the heart of Filipino cuisine, an ingredient so fundamental to the country’s food culture that it is eaten at every meal, from breakfast to dinner. - It’s served at every meal with almost every dish, including meats, seafood, vegetables, and soups. Rice also plays a significant role in Filipino celebrations and rituals and is a symbol of community and shared identity.

2. PROBLEM - A variety of fungal, bacterial, and viral diseases can infect rice plants, causing reduced yield, lower quality, and even total crop loss. These diseases can be devastating to farmers, especially those with limited resources who cannot afford the cost of chemical treatments or disease-resistant seeds. - Additionally, the high humidity and frequent rain in the Philippines create favorable conditions for the growth and spread of rice diseases.

3. RESEARCH We came across 13 rice diseases that are spread into three categories: - Fungal- affects the leaf blade, sheath, stem, node, and panicle - Bacterial- affects the leaf blade - Viral- affects the leaf blade and sheath

⚡ Click here to know the parts of a Rice Plant

List of all 13 diseases under three categories: - i. Fungal-- Rice Blast, Sheath Blight, Brown Spot, Narrow Brown Spot, Sheath Rot, Stem Rot, Bakanae, & Rice False Smut - ii. Bacterial-- Bacterial Leaf Blight & Bacterial Leaf Streak - iii. Viral-- Tungro Virus, Ragged Stunt Virus, & Grassy Stunt Virus

4. SOLUTION To build a deep-learning classifier model that can accurately detect different rice diseases.

Dengue is a viral disease spread by Aedes aegypti mosquitoes. It is a problem in many tropical and subtropical parts of the world including Africa, Southeast Asia, and South America. In the Philippines, the viral disease is still endemic in all regions wherein annual cases have ranged from 200,000 to 400,000.

In this dataset, the weekly cumulative confirmed cases of Dengue in the Philippines from January 1, 2017 to October 8, 2022 were collected from the Philippine Department of Health website. The Excel file has three sheets: Sheet 1 contains the raw data that was extracted from the DOH website; Sheet 2 contains the raw, computed (Δ(X_n-X_(n-1))), and imputed data that were used in building the ARIMA-GARCH and HW models; and, Sheet 3 contains the forecasts from the models considered.

The data are useful as they as they can be used to train predictive models that can produce short-term forecasts of Dengue cases in the Philippines. These data can provide dynamic information to health officials and other concerned departments and agencies for surveillance, analysis, policy making, and decision making. The data are reusable and can be used to further explore the dengue cases in the Philippines.

Preliminary figures for 2024 indicated that ischaemic heart disease was the leading cause of death in the Philippines. The number of people who died from this illness was estimated at 129.000. Following this, cancer resulted in the deaths of about 74,000 people. Eating habits Heart diseases have been linked to high meat consumption, among others. In the Philippines, pork has been the most consumed meat type, followed closely by chicken. While pork meat is typically produced domestically, the country also imports pork to supplement its supply. However, plant-based food has started gaining popularity among Filipinos. In fact, a 2024 survey revealed that 69 percent of surveyed Filipinos consumed plant-based products, including meat alternatives. Common diseases in the Philippines Aside from heart and cerebrovascular diseases, the Filipino population is also exposed to infections, diabetes, skin diseases, and illnesses resulting from high meat consumption. In 2020, over 700,000 Filipinos contracted acute respiratory tract infections, followed by over 400,000 diagnosed with hypertension. In areas with high exposure to rain, dengue infections and leptospirosis have also become prevalent.

Philippines PH: Cause of Death: by Non-Communicable Diseases: % of Total data was reported at 67.300 % in 2016. This records an increase from the previous number of 67.200 % for 2015. Philippines PH: Cause of Death: by Non-Communicable Diseases: % of Total data is updated yearly, averaging 66.450 % from Dec 2000 (Median) to 2016, with 4 observations. The data reached an all-time high of 67.300 % in 2016 and a record low of 56.500 % in 2000. Philippines PH: Cause of Death: by Non-Communicable Diseases: % of Total data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Philippines – Table PH.World Bank.WDI: Health Statistics. Cause of death refers to the share of all deaths for all ages by underlying causes. Non-communicable diseases include cancer, diabetes mellitus, cardiovascular diseases, digestive diseases, skin diseases, musculoskeletal diseases, and congenital anomalies.; ; Derived based on the data from WHO's Global Health Estimates.; Weighted average;

Among non-communicable diseases in the Philippines in 2023, current health expenditure was highest on diseases of the genitourinary system (nephritis) at around *** billion Philippine pesos. Meanwhile, spending on oral diseases reached **** billion in that year.

Leaf Disease

## Overview

Leaf Disease is a dataset for object detection tasks - it contains Rice Leaf Diease annotations for 1,200 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [Public Domain license](https://creativecommons.org/licenses/Public Domain).

Philippines PH: Cause of Death: by Communicable Diseases & Maternal, Prenatal & Nutrition Conditions: % of Total data was reported at 25.200 % in 2016. This records a decrease from the previous number of 25.400 % for 2015. Philippines PH: Cause of Death: by Communicable Diseases & Maternal, Prenatal & Nutrition Conditions: % of Total data is updated yearly, averaging 26.100 % from Dec 2000 (Median) to 2016, with 4 observations. The data reached an all-time high of 33.800 % in 2000 and a record low of 25.200 % in 2016. Philippines PH: Cause of Death: by Communicable Diseases & Maternal, Prenatal & Nutrition Conditions: % of Total data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Philippines – Table PH.World Bank.WDI: Health Statistics. Cause of death refers to the share of all deaths for all ages by underlying causes. Communicable diseases and maternal, prenatal and nutrition conditions include infectious and parasitic diseases, respiratory infections, and nutritional deficiencies such as underweight and stunting.; ; Derived based on the data from WHO's Global Health Estimates.; Weighted average;

Actual value and historical data chart for Philippines Cause Of Death By Non Communicable Diseases Percent Of Total

Dataset

This dataset was created by Gaurav Dutta

Contents

Actual value and historical data chart for Philippines Cause Of Death By Communicable Diseases And Maternal Prenatal And Nutrition Conditions Percent Of Total

Current health expenditures on diseases of the geniro-urinary system (nephritis) in the Philippines amounted to about *** billion Philippine pesos in 2023 -the highest among other diseases. In comparison, health spending on dengue was lowest in that year.

Forecast: Total Population Requiring Interventions Against Neglected Tropical Diseases in Philippines 2024 - 2028 Discover more data with ReportLinker!

BackgroundDespite an unknown cause, Kawasaki disease (KD) is currently the primary leading cause of acquired heart disease in developed countries in children and has been increasing in recent years. Research efforts have explored environmental factors related to KD, but they are still unclear especially in the tropics. We aimed to describe the incidence of KD in children, assess its seasonality, and determine its association with ambient air temperature in the National Capital Region (NCR), Philippines from January 2009 to December 2019.MethodsMonthly number of KD cases from the Philippine Pediatric Society (PPS) disease registry was collected to determine the incidence of KD. A generalized linear model (GLM) with quasi-Poisson regression was utilized to assess the seasonality of KD and determine its association with ambient air temperature after adjusting for the relevant confounders.ResultsThe majority of KD cases (68.52%) occurred in children less than five years old, with incidence rates ranging from 14.98 to 23.20 cases per 100,000 population, and a male-to-female ratio of 1.43:1. Seasonal variation followed a unimodal shape with a rate ratio of 1.13 from the average, peaking in March and reaching the lowest in September. After adjusting for seasonality and long-term trend, every one-degree Celsius increase in the monthly mean temperature significantly increased the risk of developing KD by 8.28% (95% CI: 2.12%, 14.80%). Season-specific analysis revealed a positive association during the dry season (RR: 1.06, 95% CI: 1.01, 1.11), whereas no evidence of association was found during the wet season (RR: 1.10, 95% CI: 0.95, 1.27).ConclusionWe have presented the incidence of KD in the Philippines which is relatively varied from its neighboring countries. The unimodal seasonality of KD and its linear association with temperature, independent of season and secular trend, especially during dry season, may provide insights into its etiology and may support enhanced KD detection efforts in the country.

Age and blood pressure data from 3500+ patient charts, following the natural disaster resulting from Typhoon Haiyan in the Philippines.The Philippines Department of Health has requested that we acknowledge the data source, which is the Department of Health, Regional Office VIII, Leyte, Philippines.

WHO: COVID-2019: Number of Patients: Death: New: Philippines data was reported at 0.000 Person in 24 Dec 2023. This stayed constant from the previous number of 0.000 Person for 23 Dec 2023. WHO: COVID-2019: Number of Patients: Death: New: Philippines data is updated daily, averaging 15.000 Person from Jan 2020 (Median) to 24 Dec 2023, with 1425 observations. The data reached an all-time high of 484.000 Person in 28 Sep 2021 and a record low of -5.000 Person in 07 Jun 2023. WHO: COVID-2019: Number of Patients: Death: New: Philippines data remains active status in CEIC and is reported by World Health Organization. The data is categorized under High Frequency Database’s Disease Outbreaks – Table WHO.D002: World Health Organization: Coronavirus Disease 2019 (COVID-2019): by Country and Region (Discontinued). Prior to 03 Feb 2020, data were generated.

Philippines PH: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Male data was reported at 32.800 NA in 2016. This records a decrease from the previous number of 32.900 NA for 2015. Philippines PH: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Male data is updated yearly, averaging 32.900 NA from Dec 2000 (Median) to 2016, with 5 observations. The data reached an all-time high of 34.400 NA in 2005 and a record low of 31.600 NA in 2000. Philippines PH: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Male data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Philippines – Table PH.World Bank.WDI: Health Statistics. Mortality from CVD, cancer, diabetes or CRD is the percent of 30-year-old-people who would die before their 70th birthday from any of cardiovascular disease, cancer, diabetes, or chronic respiratory disease, assuming that s/he would experience current mortality rates at every age and s/he would not die from any other cause of death (e.g., injuries or HIV/AIDS).; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted average;

Philippines PH: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70 data was reported at 26.800 % in 2016. This records a decrease from the previous number of 26.900 % for 2015. Philippines PH: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70 data is updated yearly, averaging 26.900 % from Dec 2000 (Median) to 2016, with 5 observations. The data reached an all-time high of 28.400 % in 2005 and a record low of 26.800 % in 2016. Philippines PH: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Philippines – Table PH.World Bank.WDI: Health Statistics. Mortality from CVD, cancer, diabetes or CRD is the percent of 30-year-old-people who would die before their 70th birthday from any of cardiovascular disease, cancer, diabetes, or chronic respiratory disease, assuming that s/he would experience current mortality rates at every age and s/he would not die from any other cause of death (e.g., injuries or HIV/AIDS).; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted average;

Philippines Infectious Disease Molecular Diagnostics Market valued at USD 390 million, driven by rising disease prevalence, advanced tech like PCR, and healthcare investments.

Project Tycho datasets contain case counts for reported disease conditions for countries around the world. The Project Tycho data curation team extracts these case counts from various reputable sources, typically from national or international health authorities, such as the US Centers for Disease Control or the World Health Organization. These original data sources include both open- and restricted-access sources. For restricted-access sources, the Project Tycho team has obtained permission for redistribution from data contributors. All datasets contain case count data that are identical to counts published in the original source and no counts have been modified in any way by the Project Tycho team. The Project Tycho team has pre-processed datasets by adding new variables, such as standard disease and location identifiers, that improve data interpretability. We also formatted the data into a standard data format.

Each Project Tycho dataset contains case counts for a specific condition (e.g. measles) and for a specific country (e.g. The United States). Case counts are reported per time interval. In addition to case counts, datasets include information about these counts (attributes), such as the location, age group, subpopulation, diagnostic certainty, place of acquisition, and the source from which we extracted case counts. One dataset can include many series of case count time intervals, such as "US measles cases as reported by CDC", or "US measles cases reported by WHO", or "US measles cases that originated abroad", etc.

Depending on the intended use of a dataset, we recommend a few data processing steps before analysis: - Analyze missing data: Project Tycho datasets do not include time intervals for which no case count was reported (for many datasets, time series of case counts are incomplete, due to incompleteness of source documents) and users will need to add time intervals for which no count value is available. Project Tycho datasets do include time intervals for which a case count value of zero was reported. - Separate cumulative from non-cumulative time interval series. Case count time series in Project Tycho datasets can be "cumulative" or "fixed-intervals". Cumulative case count time series consist of overlapping case count intervals starting on the same date, but ending on different dates. For example, each interval in a cumulative count time series can start on January 1st, but end on January 7th, 14th, 21st, etc. It is common practice among public health agencies to report cases for cumulative time intervals. Case count series with fixed time intervals consist of mutually exclusive time intervals that all start and end on different dates and all have identical length (day, week, month, year). Given the different nature of these two types of case count data, we indicated this with an attribute for each count value, named "PartOfCumulativeCountSeries".

Philippine Health Indicators

Essential, Neglected, Quick, and All Health Topics

By Humanitarian Data Exchange [source]

About this dataset

This dataset provides comprehensive data on a variety of indicators related to health, medical equipment, and other social determinants found in the Philippines. It contains information from World Health Organization's data portal with insights from Mortality and global health estimates, Sustainable development goals, Millennium Development Goals (MDGs), Health systems, Malaria, Tuberculosis, Child health, Infectious diseases and more. This valuable dataset can be used to explore human behavior in regards to public and environmental health as well as for research into world-wide trends in healthcare access. The data can help inform better policies that protect public safety and improve long-term positive outcomes such as reducing risk factors of disease or increasing resilience against natural disasters. This collection is an invaluable resource for understanding the drivers of healthcare disparities between countries while promoting transparency within governments worldwide

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How to use the dataset

This dataset provides information on health indicators in the Philippines. It contains data from the World Health Organization's (WHO) data portal covering a broad range of topics such as mortality and global health estimates, Sustainable Development Goals (SDGs), Millennium Development Goals (MDGs), Health Systems, Malaria, Tuberculosis, Child Health, Infectious Diseases, World Health Statistics and other subtopics. This dataset can be used to generate reports and trends on the wellbeing of Filipinos and health services available in the nation.

To use this dataset: - Decide which indicator or indicators you want to analyze - You can select specific indicators by filtering with GHO code number or name; DATASOURCE code or name; PUBLISHSTATE code or name; YEAR codes; REGION codes; COUNTRY codes etc., - Once you separate out your chosen indicator(s), it is time to access more detailed information about said indicators. Look for a related URL link provided in the Database: these URLs link to more detailed information such as description of variables used in surveys/studies taken from medical professionals that was used to compile this data set.. The easiest way would be check for URLS associated with particular Indicator Code because each URL should provide specific additional info regarding variable definitions of different criteria related to said Indicator Code selected as well details on methodologies used etc.,
- Determine how one wants visualize - some will prefer tables while others pictures/graphs/charts which are easy compare visually with little effort possible if using known standard image export software program (+ its present settings whether it be excel sheets , PDF format files etc). You can also customize how one wish visualize wanted results using settings options provided depending on what comparison(s) one wishes look at when it come any visual aids that may help better understand results found its included text fields inside of records too… whatever types visualization wished use – goal attempt gain better understanding questions asked during assortment phases before start analysis phase so once analysis performed “next course” action plan can formulated having seen collected sample sizes been compared against each other.....

• Finally analyze results found from visualization step- Take note: caution should taken when interpreting given what comparing . Results are not guarantee causal effect between two things just because correlation exists between variables selected outcomes present themselves during duration view presented chart field usage! Make sure look at extra detail fields too during evaluation possible correlations sometimes hidden amongst particular hidden categories within given

Research Ideas

• Analyzing the regional differences in medical equipment usage and health outcomes across the Philippines
• Tracking changes in medical equipment availability over time in different parts of the country
• Investigating how access to medical equipment varies between specific types of healthcare providers

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

**File: injuries-and-violence-indicators-for-philippines-47.c...