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1. Comprehensive assessment, review, and comparison of AI models for solar irradiance prediction based on different time/estimation intervals

Author

Olusola Bamisile, Dongsheng Cai, Ariyo Oluwasanmi, Chukwuebuka Ejiyi, Chiagoziem C. Ukwuoma, Oluwasegun Ojo, Mustapha Mukhtar, and Qi Huang

Subjects
Medicine, Science
Abstract

Abstract Solar energy-based technologies have developed rapidly in recent years, however, the inability to appropriately estimate solar energy resources is still a major drawback for these technologies. In this study, eight different artificial intelligence (AI) models namely; convolutional neural network (CNN), artificial neural network (ANN), long short-term memory recurrent model (LSTM), eXtreme gradient boost algorithm (XG Boost), multiple linear regression (MLR), polynomial regression (PLR), decision tree regression (DTR), and random forest regression (RFR) are designed and compared for solar irradiance prediction. Additionally, two hybrid deep neural network models (ANN-CNN and CNN-LSTM-ANN) are developed in this study for the same task. This study is novel as each of the AI models developed was used to estimate solar irradiance considering different timesteps (hourly, every minute, and daily average). Also, different solar irradiance datasets (from six countries in Africa) measured with various instruments were used to train/test the AI models. With the aim to check if there is a universal AI model for solar irradiance estimation in developing countries, the results of this study show that various AI models are suitable for different solar irradiance estimation tasks. However, XG boost has a consistently high performance for all the case studies and is the best model for 10 of the 13 case studies considered in this paper. The result of this study also shows that the prediction of hourly solar irradiance is more accurate for the models when compared to daily average and minutes timestep. The specific performance of each model for all the case studies is explicated in the paper.

Published
2022
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2. Comprehensive functional data analysis of China’s dynamic energy security index

Author

Olusola Bamisile, Oluwasegun Ojo, Nasser Yimen, Humphrey Adun, Jian Li, Sandra Obiora, and Qi Huang

Subjects
China, Energy investment, Energy security, Functional data analysis, Regression, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

In the transition towards a cleaner and more sustainable global energy production and consumption, the energy security index (ESI) is an integral deciding factor for many countries. In this paper, the dynamics of China’s energy security index is constructed and analyzed using comprehensive functional data analysis (FDA) techniques. The energy security indices of 30 provinces in China between 2004 and 2017 are considered. The effect of investment (in energy sector) on energy security of the provinces is also checked using functional regression models. The average ESI for Shanxi (0.64737), Inner Mongolia (0.64540), and Shaanxi (0.51039), and the energy investment in Inner Mongolia, Shandong, and Shanxi form the top 10% of all the provinces considered. Exploratory analysis shows that there was an overall decrease in ESI in most of the provinces between 2004 and 2017, despite an increase in the investment in energy sectors of these provinces in the same period. The functional regression models show that investment in energy sector achieved its highest positive effect on energy security between year 2010 and 2014.

Published
2021
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3. The CoronaSurveys System for COVID-19 Incidence Data Collection and Processing

Author

Carlos Baquero, Paolo Casari, Antonio Fernandez Anta, Amanda García-García, Davide Frey, Augusto Garcia-Agundez, Chryssis Georgiou, Benjamin Girault, Antonio Ortega, Mathieu Goessens, Harold A. Hernández-Roig, Nicolas Nicolaou, Efstathios Stavrakis, Oluwasegun Ojo, Julian C. Roberts, and Ignacio Sanchez

Subjects
COVID-19, monitoring, survey, indirect reporting, visualization, network scale-up method, Electronic computers. Computer science, QA75.5-76.95
Abstract

CoronaSurveys is an ongoing interdisciplinary project developing a system to infer the incidence of COVID-19 around the world using anonymous open surveys. The surveys have been translated into 60 languages and are continuously collecting participant responses from any country in the world. The responses collected are pre-processed, organized, and stored in a version-controlled repository, which is publicly available to the scientific community. In addition, the CoronaSurveys team has devised several estimates computed on the basis of survey responses and other data, and makes them available on the project’s website in the form of tables, as well as interactive plots and maps. In this paper, we describe the computational system developed for the CoronaSurveys project. The system includes multiple components and processes, including the web survey, the mobile apps, the cleaning and aggregation process of the survey responses, the process of storage and publication of the data, the processing of the data and the computation of estimates, and the visualization of the results. In this paper we describe the system architecture and the major challenges we faced in designing and deploying it.

Published
2021
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4. Estimating the COVID-19 Prevalence in Spain With Indirect Reporting via Open Surveys

Author

Augusto Garcia-Agundez, Oluwasegun Ojo, Harold A. Hernández-Roig, Carlos Baquero, Davide Frey, Chryssis Georgiou, Mathieu Goessens, Rosa E. Lillo, Raquel Menezes, Nicolas Nicolaou, Antonio Ortega, Efstathios Stavrakis, and Antonio Fernandez Anta

Subjects
COVID-19, pandemic, serology, survey, indirect reporting, sensing, Public aspects of medicine, RA1-1270
Abstract

During the initial phases of the COVID-19 pandemic, accurate tracking has proven unfeasible. Initial estimation methods pointed toward case numbers that were much higher than officially reported. In the CoronaSurveys project, we have been addressing this issue using open online surveys with indirect reporting. We compare our estimates with the results of a serology study for Spain, obtaining high correlations (R squared 0.89). In our view, these results strongly support the idea of using open surveys with indirect reporting as a method to broadly sense the progress of a pandemic.

Published
2021
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5. A Snapshot of COVID-19 Incidence, Hospitalizations, and Mortality from Indirect Survey Data in China in January 2023

Author

Juan M. Ramírez, Sergio Díaz-Aranda, Jose Aguilar, Oluwasegun Ojo, Rosa Elvira Lillo, and Antonio Fernández Anta

Abstract

In this work we estimate the incidence of COVID-19 in China using online indirect surveys (which preserve the privacy of the participants). The indirect surveys deployed collect data on the incidence of COVID-19, asking the participants about the number of cases, deaths, vaccinated, and hospitalized that they know. The incidence of COVID-19 (cases, deaths, etc.) is then estimated using a modified Network Scale-up Method (NSUM). Survey responses (100, 200 and 1,000, respectively) were collected from Australia, the UK, and China in January 2023. The estimates in Australia and the UK are compared with official data, showing that they are in the confidence intervals or rather close. Cronbach’s alpha values also indicate good confidence in the estimates. The estimates obtained in China are, among others, that 91% of the population is vaccinated, almost 80% had been infected in the last month, and almost 3% in the last 24 hours.

Published
2023

6. Detecting and classifying outliers in big functional data

Author

Oluwasegun Ojo, Carlo Sguera, Rosa E. Lillo, and Antonio Fernández Anta

Subjects
FOS: Computer and information sciences, Statistics and Probability, Measure (data warehouse), business.industry, Computer science, Applied Mathematics, 2R10 (Functional data analysis), Magnitude (mathematics), Pattern recognition, Sample (statistics), Statistics - Applications, Computer Science Applications, Methodology (stat.ME), Correlation, ComputingMethodologies_PATTERNRECOGNITION, Simulated data, Outlier, Linear regression, Applications (stat.AP), Anomaly detection, Artificial intelligence, business, Statistics - Methodology
Abstract

We propose two new outlier detection methods, for identifying and classifying different types of outliers in (big) functional data sets. The proposed methods are based on an existing method called Massive Unsupervised Outlier Detection (MUOD). MUOD detects and classifies outliers by computing for each curve, three indices, all based on the concept of linear regression and correlation, which measure outlyingness in terms of shape, magnitude and amplitude, relative to the other curves in the data. ‘Semifast-MUOD’, the first method, uses a sample of the observations in computing the indices, while ‘Fast-MUOD’, the second method, uses the point-wise or $$L_1$$ median in computing the indices. The classical boxplot is used to separate the indices of the outliers from those of the typical observations. Performance evaluation of the proposed methods using simulated data show significant improvements compared to MUOD, both in outlier detection and computational time. We show that Fast-MUOD is especially well suited to handling big and dense functional datasets with very small computational time compared to other methods. Further comparisons with some recent outlier detection methods for functional data also show superior or comparable outlier detection accuracy of the proposed methods. We apply the proposed methods on weather, population growth, and video data.

Published
2021

7. Estimating Active Cases of COVID-19

Author

Javier Álvarez, Carlos Baquero, Elisa Cabana, Jaya Prakash Champati, Antonio Fernández Anta, Davide Frey, Augusto Garcia-Agundez, Chryssis Georgiou, Mathieu Goessens, Harold Hernández, Rosa Lillo, Raquel Menezes, Raúl Moreno, Nicolas Nicolaou, Oluwasegun Ojo, Antonio Ortega, Estrella Rausell, Jesús Rufino, Efstathios Stavrakis, Govind Jeevan, and Christin Glorioso

Abstract

Introduction: Having accurate and timely data on active COVID-19 cases is challenging, since it depends on the availability of an appropriate infrastructure to perform tests and aggregate their results. In this work, we propose alternative methods to assess the number of active cases of COVID-19. Methods: We consider a case to be active if it is infectious, and we propose three methods to estimate the number of active infectious cases of COVID-19 using (1) official publications based on PCR testing, (2) fatalities using the case fatality ratio, and (3) three public surveys from the Delphi Group at Carnegie Mellon University, the University of Maryland, and our self-developed survey entitled Coronasurveys. Results: We show that public surveys are a viable option in countries with reduced testing capacity or infrastructures.

Published
2021

8. Estimating the COVID-19 Prevalence in Spain With Indirect Reporting via Open Surveys

Author

Antonio Fernández Anta, Harold Hernández, Rosa E. Lillo, Chryssis Georgiou, Antonio Ortega, Nicolas Nicolaou, Raquel Menezes, Davide Frey, Oluwasegun Ojo, Carlos Baquero, Augusto Garcia-Agundez, Mathieu Goessens, Efstathios Stavrakis, Technische Universität Darmstadt (TU Darmstadt), Institute IMDEA Networks [Madrid], Universidad Carlos III de Madrid [Madrid] (UC3M), Universidade do Minho = University of Minho [Braga], the World Is Distributed Exploring the tension between scale and coordination (WIDE), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-SYSTÈMES LARGE ÉCHELLE (IRISA-D1), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-CentraleSupélec-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Rennes (ENS Rennes)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS), Cyprus University of Technology, Chercheur indépendant, Algolysis [Cyprus], USC Viterbi School of Engineering, University of Southern California (USC), Technische Universität Darmstadt - Technical University of Darmstadt (TU Darmstadt), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Universidade do Minho, Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), and Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1)

Subjects
2019-20 coronavirus outbreak, Saúde de qualidade, Coronavirus disease 2019 (COVID-19), Matemáticas, Medicina, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), serology, Estadística, indirect reporting, 030204 cardiovascular system & hematology, 03 medical and health sciences, 0302 clinical medicine, Seroepidemiologic Studies, Surveys and Questionnaires, Statistics, Pandemic, Prevalence, Humans, survey, 030212 general & internal medicine, Survey, Disease Notification, Pandemics, Biología y Biomedicina, sensing, Ciências Naturais::Matemáticas, Informática, Telecomunicaciones, Science & Technology, lcsh:Public aspects of medicine, pandemic, [INFO.INFO-CE]Computer Science [cs]/Computational Engineering, Finance, and Science [cs.CE], COVID-19, lcsh:RA1-1270, Brief Research Report, Serology, Geography, Spain, Indirect reporting, Sensing, Public Health, Covid-19, Estimation methods
Abstract

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found at: https://github.com/GCGImdea/coronasurveys., During the initial phases of the COVID-19 pandemic, accurate tracking has proven unfeasible. Initial estimation methods pointed toward case numbers that were much higher than officially reported. In the CoronaSurveys project, we have been addressing this issue using open online surveys with indirect reporting. We compare our estimates with the results of a serology study for Spain, obtaining high correlations (R squared 0.89). In our view, these results strongly support the idea of using open surveys with indirect reporting as a method to broadly sense the progress of a pandemic., At the time of writing this article, Corona Surveys has received no public funding. Social networks surveys have been partially funded via donations through our website. CoronaSurveys received an award from the UMD/CMU COVID-19 Symptom Data Challenge.

Published
2021

9. CoronaSurveys: Using Surveys with Indirect Reporting to Estimate the Incidence and Evolution of Epidemics

Author

Oluwasegun Ojo, Augusto García-Agundez, Benjamin Girault, Harold Hernández, Elisa Cabana, Amanda García-García, Payman Arabshahi, Carlos Baquero, Paolo Casari, Ednaldo José Ferreira, Davide Frey, Chryssis Georgiou, Mathieu Goessens, Anna Ishchenko, Ernesto Jiménez, Oleksiy Kebkal, Rosa Lillo, Raquel Menezes, Nicolas Nicolaou, Antonio Ortega, Paul Patras, Roberts, Julian C., Efstathios Stavrakis, Yuichi Tanaka, Antonio Fernández Anta, the World Is Distributed Exploring the tension between scale and coordination (WIDE), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-SYSTÈMES LARGE ÉCHELLE (IRISA-D1), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Institut National de Recherche en Informatique et en Automatique (Inria), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), and Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique)

Subjects
FOS: Computer and information sciences, Computer Science - Computers and Society, surveys, Computer Science - Distributed, Parallel, and Cluster Computing, Computers and Society (cs.CY), [INFO.INFO-CE]Computer Science [cs]/Computational Engineering, Finance, and Science [cs.CE], crowdsourcing, Applications (stat.AP), Distributed, Parallel, and Cluster Computing (cs.DC), privacy, Statistics - Applications, epidemics
Abstract

The world is suffering from a pandemic called COVID-19, caused by the SARS-CoV-2 virus. National governments have problems evaluating the reach of the epidemic, due to having limited resources and tests at their disposal. This problem is especially acute in low and middle-income countries (LMICs). Hence, any simple, cheap and flexible means of evaluating the incidence and evolution of the epidemic in a given country with a reasonable level of accuracy is useful. In this paper, we propose a technique based on (anonymous) surveys in which participants report on the health status of their contacts. This indirect reporting technique, known in the literature as network scale-up method, preserves the privacy of the participants and their contacts, and collects information from a larger fraction of the population (as compared to individual surveys). This technique has been deployed in the CoronaSurveys project, which has been collecting reports for the COVID-19 pandemic for more than two months. Results obtained by CoronaSurveys show the power and flexibility of the approach, suggesting that it could be an inexpensive and powerful tool for LMICs., Comment: Presented at The KDD Workshop on Humanitarian Mapping, San Diego, California USA, August 24, 2020

Published
2021

10. Measuring Icebergs: Using Different Methods to Estimate the Number of COVID-19 Cases in Portugal and Spain

Author

Paolo Casari, Chryssis Georgiou, Nicolas Nicolaou, Davide Frey, Paul Patras, Raquel Menezes, Oluwasegun Ojo, Carlos Baquero, Augusto Garcia-Agundez, and Antonio Fernández Anta

Subjects
Geography, Coronavirus disease 2019 (COVID-19), Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Pandemic, Econometrics, Discount points, Limited resources
Abstract

The world is suffering from a pandemic called COVID-19, caused by the SARS-CoV-2 virus. The different national governments have problems evaluating the reach of the epidemic, having limited resources and tests at their disposal. Hence, any means to evaluate the number of persons with symptoms compatible with COVID-19 with reasonable level of accuracy is useful. In this paper we present the initial results of the @CoronaSurveys project. The objective of this project is the collection and publication of data concerning the number of people that show symptoms compatible with COVID-19 in different countries using open anonymous surveys. While this data may be biased, we conjecture that it is still useful to estimate the number of infected persons with the COVID-19 virus at a given point in time in these countries, and the evolution of this number over time. We show here the initial results of the @CoronaSurveys project in Spain and Portugal.

Published
2020

11. Role of Extension Forest Officers in Forest Conservation in Cross River State, Nigeria

Author

Ejeje Igwe Agube, Edwin M Igbokwe, and Felix Oluwasegun Ojo

Subjects
Agriculture (General), S1-972
Abstract

The study assessed the roles of forestry extension service in forest conservation in Cross River State. The specific objectives were to: examine extension role of forest officers in forest conservation; identify forest officers’ extension training needs in forest conservation; and identify forest officers’ perceived constraints militating against forestry extension service delivery. All the forest officers under the employ of the Cross River State Forestry Commission (CRSFC) formed the population of the study. Systematic sampling technique was employed to select 78 respondents for the study. Data were collected through validated structured interview schedule. Data were analyzed using percentage and mean scores. The major effective extension role of forest officers in forest conservation were found to include, creating awareness through environmental enlightenment campaign in forest communities (M = 2.61) and dissemination of information in relation to public participation in forest conservation (M = 2.61), among other roles. Major perceived training needs of forest officers in forest conservation were sustainable forest management practices (M = 3.39), forestry extension practices (M = 3.32) and developing group participation. Major constraints to effective forestry extension service delivery were found to include inadequate funding (M = 2.97), and lack of vehicles for field staff logistics (M = 2.80). Forestry extension service delivery in forest conservation was less effective, owing to a great number of constraints militating against the service delivery in forest conservation. For the effectiveness of forestry extension services in the state, the constraints to the service delivery must be seriously considered. Keywords: Forestry, Extension, Service, Conservation, Training, Needs, Constraints

Published
2021

12. Food Security Situations among Female Headed Households in Enugu East Senatorial Zone of Enugu State, Nigeria

Author

Remigius Ikechukwu Ozioko, Chioma Blessing Nwigwe, Ifeanyi Miracle Nwafor, Onyinyechi Ifeanyi Nnadi, Anthonia Ngozi Asadu, Chukwuemeka Chiebonam Onyia, Innocent Achonam Enwelu, and Felix Oluwasegun Ojo

Subjects
Agriculture (General), S1-972
Abstract

Abstract This study ascertained food security situation of female-headed households in Enugu State, Nigeria. The study objectives were to identify livelihood activities of respondents, food security situation, factors that affect food security, and coping strategies of respondents. Multi-stage, snow ball and simple random sampling techniques were used to select 72 female-headed households. Data were analyzed using mean score and factor analysis. Results show that respondents’ livelihood activities include crop production and processing. Household food security situations included food secure to moderately food-insecure. Major factors faced by respondents were poor nutrition and poverty. Coping strategies includes restriction of food consumption of adults ( = 0.609). Hence, government and other development agencies should provide policies and efforts that assist FHH in capacity building like providing productive resources.

Published
2020

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