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16 results on '"Negash, Yohannes"'

1. Coronavirus and Paramyxovirus Shedding by Bats in a Cave and Buildings in Ethiopia

Author

Lane, Jennifer K, Negash, Yohannes, Randhawa, Nistara, Kebede, Nigatu, Wells, Heather, Ayalew, Girma, Anthony, Simon J, Smith, Brett, Goldstein, Tracey, Kassa, Tesfu, Mazet, Jonna AK, Consortium, PREDICT, and Smith, Woutrina A

Subjects
Biological Sciences, Ecology, Emerging Infectious Diseases, Vaccine Related, Infectious Diseases, Biodefense, Prevention, Biotechnology, Infection, Good Health and Well Being, Animals, COVID-19, Chiroptera, Ethiopia, Genome, Viral, Humans, Phylogeny, SARS-CoV-2, Viruses, bat, coronavirus, paramyxovirus, cave, viral shedding, Veterinary Sciences, Public Health and Health Services, Veterinary sciences
Abstract

Bats are important hosts of zoonotic viruses with pandemic potential, including filoviruses, MERS-Coronavirus (CoV), SARS-CoV -1, and likely SARS-CoV-2. Viral infection and transmission among wildlife are dependent on a combination of factors that include host ecology and immunology, life history traits, roosting habitats, biogeography, and external stressors. Between 2016 and 2018, four species of insectivorous bats from a readily accessed roadside cave and buildings in Ethiopia were sampled and tested for viruses using consensus PCR assays for five viral families/genera. Previously identified and novel coronaviruses and paramyxoviruses were identified in 99 of the 589 sampled bats. Bats sampled from the cave site were more likely to test positive for a CoV than bats sampled from buildings; viral shedding was more common in the wet season; and rectal swabs were the most common sample type to test positive. A previously undescribed alphacoronavirus was detected in two bat species from different taxonomic families, sampling interfaces, geographic locations, and years. These findings expand knowledge of the range and diversity of coronaviruses and paramyxoviruses in insectivorous bats in Ethiopia and reinforce that an improved understanding of viral diversity and species-specific shedding dynamics is important for designing informed zoonotic disease surveillance and spillover risk reduction efforts.

Published
2022

2. Clinical sensitivity and time-to-result of a cascaded pooled testing approach for assessing the prevalence and intensity of Schistosoma haematobium infection.

Author

Degarege, Abraham, Levecke, Bruno, Negash, Yohannes, Animut, Abebe, and Erko, Berhanu

Subjects
SCHISTOSOMA haematobium, SCHOOL children, URINALYSIS, URINE, DIAGNOSIS methods
Abstract

Background: This study compared the clinical sensitivity and the time-to-result of an individual testing (IT) and a cascaded pooled testing approach (CPT; a positive test result in a pooled sample triggers examination of smaller-sized pools or individual samples) for assessing the prevalence and the intensity of Schistosoma haematobium infection. We also compared the sensitivity of the CPT in detecting S. haematobium infection when deploying urine filtration microscopy (UFM) vs. urine reagent strips (URS), and testing 10 mL vs. 15 mL of urine. Methodology/Principal findings: Between October 2021 and April 2022, S. haematobium eggs were counted in urine samples collected from school-aged children living in the Afar and Gambella Regional States of Ethiopia. Urine samples were collected at baseline (n = 1,288), and one month after administration of praziquantel (n = 118). All urine samples were processed through both an IT and a CPT approach (pools of 5, 10, 20, and 40 individual samples), deploying UFM (10 mL) and URS (10 mL). In addition, 15 mL urine was processed through the CPT deploying UFM. At baseline, the prevalence of S. haematobium infection estimated when using UFM and deploying a CPT approach was significantly lower (17.3%) compared to an IT approach (31.5%). The clinical sensitivity of the CPT in detecting S. haematobium eggs was 51.7%. The sensitivity increased significantly as a function of increasing log transformed urine egg counts (UECs) of the individual samples (OR 2.71, 95%CI 1.63 ― 4.52). The sensitivity was comparable when the amount of urine examined was 10 mL (51.7%) vs. 15 ml (50.8%), and when UFM was used for testing vs. URS (51.5%). The mean log UECs estimated following the CPT approach was lower compared to the estimate by the IT (p <0.001). UECs of the individual samples estimated using the IT and CPT approaches were moderately correlated (r = 0.59 when 10 mL and 15 mL urine was examined after pooling). CPT reduced the time needed for processing urine samples and testing for S. haematobium infection by 29% with UFM and by 27.7% with URS. Conclusions/Significance: CPT based on UFM and URS techniques may help to rapidly identify areas with higher prevalence of S. haematobium infection (hotspots) in a population. However, the performance of this approach in estimating the prevalence of infection may be compromised, particularly in endemic areas with low intensity infection. Author summary: We examined the sensitivity and the time-to-result of a cascaded pooled testing approach for detecting and estimating the intensity of Schistosoma haematobium infection. Urine samples collected from school-aged children in the Afar and Gambella regions of Ethiopia were analyzed individually and in pools using both urine filtration microcopy (UFM) and urine reagent strips (URS). The cascaded pooled testing approach detected close to 52% of urine samples with S. haematobium eggs. The sensitivity increased as the egg count of the individual samples increased but was comparable when the amount of urine examined was 10 mL vs. 15 mL, and when the diagnostic test used was UFM vs. URS. Pooled testing reduced the time needed for processing urine samples and counting S. haematobium eggs by 29%. Pooled testing based on UFM may help rapidly identify areas with higher prevalence of S. haematobium infection in a population. [ABSTRACT FROM AUTHOR]

Published
2024
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3. Evaluating the Performance of FlukeCatcher at Detecting Urogenital Schistosomiasis.

Author

Fok, Louis, Erko, Berhanu, Brett-Major, David, Animut, Abebe, Broadhurst, M. Jana, Dai, Daisy, Linville, John, Levecke, Bruno, Negash, Yohannes, and Degarege, Abraham

Subjects
SCHISTOSOMIASIS, SCHISTOSOMA haematobium, TEST methods, URINE, EGGS
Abstract

Urine filtration microscopy (UFM) lacks sensitivity in detecting low-intensity Schistosoma haematobium infections. In pursuit of a superior alternative, this study evaluated the performance of FlukeCatcher microscopy (FCM) at detecting S. haematobium eggs in human urine samples. Urine samples were collected from 572 school-age children in Afar, Ethiopia in July 2023 and examined using UFM and FCM approaches. Using the combined UFM and FCM results as a reference, the sensitivity, negative predictive value, and agreement levels for the two testing methods in detecting S. haematobium eggs in urine samples were calculated. The sensitivity and negative predictive value of detecting S. haematobium eggs in urine samples for FCM was 84% and 97%, respectively, compared to 65% and 93% for UFM. The FCM test results had an agreement of 61% with the UFM results, compared to 90% with the combined results of FCM and UFM. However, the average egg count estimates were lower when using FCM (6.6 eggs per 10 mL) compared to UFM (14.7 eggs per 10 mL) (p < 0.0001). Incorporating FCM into specimen processing could improve the diagnosis of S. haematobium infection but may underperform in characterizing the intensity of infection. [ABSTRACT FROM AUTHOR]

Published
2024
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4. Assessment of Urogenital Schistosomiasis Knowledge, Attitudes and Practices Among Abobo Communities in Gambella Regional State, Southwestern Ethiopia.

Author

Mohammed, Tigist, Degarege, Abraham, Aemero, Mulugeta, Animut, Abebe, Negash, Yohannes, and Erko, Berhanu

Abstract

Background: Knowledge about community awareness and practices related to a disease, among other things, helps to plan relevant control strategies. This study assessed the knowledge, attitude, and practices (KAPs) of the community in Abobo district of Gambella Regional State in Southwestern Ethiopia about urogenital schistosomiasis (UGS), which is endemic to the region. Methods: A community-based cross-sectional study was conducted in 2022. A pretested structured questionnaire was administered. Multivariable logistic regression was used to examine factors associated with KAPs variables. Results: Most study participants (90.6%) responded they have previously heard of UGS. Over 95% of the participants knew at least 1 symptom of UGS and 30% knew the transmission cycle. About 15.9% and 26.8% knew keeping environmental hygiene and refraining from using cercariae infested water, as preventive methods, respectively. Over half of the participants (50.1%) disagreed or did not know that urinating close to the river/dam water contributes to transmission, and almost all participants had contact with the dam/river water in one way or another. Education status and history of having the infection were the key significant correlates of most KAPs variables. Conclusion: Most of the community members had poor knowledge and attitudes about the mode of transmission and preventive measures of UGS. Thus, besides school-based mass drug administration (MDA), UGS control efforts in the region should incorporate health education in conjunction with safe water supplies, and provision of sanitary facilities to effectively reduce the transmission of the disease. [ABSTRACT FROM AUTHOR]

Published
2024
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10. Socializing One Health: an innovative strategy to investigate social and behavioral risks of emerging viral threats

Author

Saylors, Karen, Wolking, David J., Hagan, Emily, Martinez, Stephanie, Francisco, Leilani, Euren, Jason, Olson, Sarah H., Miller, Maureen, Fine, Amanda E., Thanh, Nga Nguyen Thi, Tran Minh, Phuc, Kalengkongan, Jusuf D., Kusumaningrum, Tina, Latinne, Alice, Pamungkas, Joko, Safari, Dodi, Saputro, Suryo, Bamba, Djeneba, Coulibaly, Kalpy Julien, Dosso, Mireille, Laudisoit, Anne, N'guettia Jean, Kouassi Manzan, Dutta, Shusmita, Islam, Ariful, Shano, Shahanaj, Mwanzalila, Mwokozi I., Trupin, Ian P., Gbakima, Aiah, Bangura, James, Yondah, Sylvester T., Karmacharya, Dibesh, Shrestha, Rima D., Kamta, Marcelle Annie Matsida, Mouiche, Mohamed Moctar Mouliom, Ndolo, Hilarion Moukala, Niama, Fabien Roch, Onikrotin, Dionne, Daszak, Peter, Johnson, Christine K., Mazet, Jonna A. K., Abaneh, Ola, Ababneh, Mustafa, Rafia, Jum, Sukor, Abd, Abdullah, Mohd Lufti, Abedin, Josefina, Abu-Basha, Ehab, Ali, Mohamed, Beal Akoundze, Junior, Akpaki, Joel, Al Hanandeh, Sief Addeen, Al Omari, Bilal, Al Shakil, Abdullah, Al-Zghoul, Mohammed, Albart, Stephenie Ann, Alshammari, Abdullah, Amarneh, Basil H., Ampofo, William, Andrew, Victoria, Ahn, Dao Le, Ankhanbaatar, Ulaankhuu, Anthony, Simon, Antonjaya, Ungke, Araya, Kidan, Arku, Jallah, Arshat, Norsharina, Asigbee, Theodore, Aung, Ohnmar, Awuni, Joseph, Ayukebong, James, Azian, Mohammed, Aziz, Nor Adilah, Ba, Aminata, Bassan, Ganzorig, Bagato, Ola, Bamba, Aboubacar, Bamba, Djenba, Barkhasbaatar, Ariunbaatar, Barrera, June, Basaraba, Cale, Bel-nono, Samuel, Belaganahalli, Manjunatha, Belay, Desalgen, Belkharia, Jaber, Binol, Ridzki M. F., Bird, Brian, Bista, Manisha, Biswas, Pitu, Blake, Matthew, Boatemaa, Linda, Bonason, Margret, Brandful, James, Brown, Joseph, Brownstein, John, Camara, Mamadi, Camara, Salif, Chai, Daniel, Chakraborty, Debapriyo, Chale, Hannah, Chaudhary, Ashok, Chea, Sokha, Chmura, Aleksei, Goossens, Benoit, Chow, Andrew, Churchill, Carolina, Commey, Abraham, Couacy-Hymann, Emmanuel, Coulibaly, Julien Kalpy, Cranfield, Michael, Damanik, Wirda, Damdinjav, Batchullum, Danial, Norhidayah, Dasak, Peter, David, Runie, Dawson, Patrick, Dembele, Arjouma, Deme, Awa, Desmond, James, Dewantari, Arghianditya Kresno, Dhanota, Jaseet, Dhole, Tapan, Diep, Nguyen Thi, Dionkounda, Artistide, Diop, Gaye Laye, Dodd, Kimberly, Dogby, Otilia, Dorjnyam, Tumendemberel, Dosso, Mireielle, Doumbouya, Kalil, Doumbouya, Mohamed Idriss, Doyle, Megan, Dramou, Simone, Drazenovich, Tracy, Duc Ahn, Dang, Duc Luu, Bach, Duendkae, Prateep, Duoc, Vu Trong, Duong, Tran Nhu, Duong, Veasna, Dursman, Huda, Dussart, Phillipe, Ee, Tan Jun, Ekiri, Abel, El Rifay, Amira S., El Shesheny, Rabel, El Taween, Ahmed N., Emmanuel, Zena Babu, Epstein, Jonathan H., Evans, Tierra Smiley, Fahmawi, Alaa, Fahn, Simeon, Feferholtz, Yasha, Ferdous, Jinnat, Fine, Amanda, Flora, Meerjady, Fransisco, Leilani, Fui Fui, Lem, Gabourie, Taylor, Gani, Millawati, Garbo, Michael, Gardner, Nicole, Gbamele, Marie, Ge, Xingyi, Gee, Lee Heng, Genovese, Brooke, Gibson, Alexandra, Gilardi, Kirsten, Gilbert, Martin, Gillis, Amethyst, Ginsos, Andrew, Godji Gnabro, Privat, Goldstein, Tracey, Gomaa, Moktar, Gomis, Jules, Gonzalez, Kevin, Grange, Zoe, Greig, Denise, Grodus, Michael, Gueu, Kpon Kakeuma Romeo, Gutierrez, Leticia, Haba, Dan Marcelin, Hamid, Suraya, Harris, Daniel K., Hashim, Abdul Kadir Abu, Hassan, Moushumi, Hassan, Quazar Nizamuddin, He, Qun, Hemachudha, Thiravat, Henry, Helen, Herbert, Ronald, Hijazeen, Zaidoun, Hilarion, Moukala Ndolo, Hill, Rebecca, Hoa, Nguyen Thi, Horwood, Paul, Hossain, Md. Enayet, Hossain, Saddam, Htun, Moh Moh, Hu, Ben, Hughes, Tom, Hul, Vibol, Van, Vo, Hussein, Fatima, Indola, Ghislain Dzeret, Iskandriati, Diah, Islam, Md. Tarikul, Islam, Shariful, Isnaim Ismail, Mohd, Ismail, Zuhair Bani, Iyanya, Jacques, Jaimin, Joel Judson, Jambai, Amara, Japning, Jeffrine Rovie Ryan, Japrin, Alexter, Jean Louis, Frantz, Joe, Titus, Johnson, Erica, Joly, Damien, Joshi, Jyotsna, Kalengkongan, Jusuf, Kalivogui, Douokoro, Kamara-Chieyoe, Nenneh, Kamau, Joseph, Kambale Syaluha, Eddy, Kandeil, Ahmed, Kane, Yogouba, Karesh, William, Kargo, Kandeh, Kasenda, Novie, Kayali, Ghazi, Kayed, Ahmed S., Kazwala, Rudovick, Ke, Changwen, Keates, Lucy, Kebede, Nigatu, Khamphaphongphane, Bouaphanh, Kheong, Chong Chee, Kilonzo, Christopher, Koffa, Ma-Sue, Kollie, Amos G., Kondiano, Marcel Sidik, Koropo, Michel, Kouamé Kouakou, Valere, Kouassi Koffi, Eugene, Kourouma, Mariam, Koutate, Abdoulaye Ousmane, Kowel, Citra Liv, Krou, Hermann Assemien, Kumakamba, Charles, Kutkat, Omnia, Lamah, François, Lan, Nguyen Thi, Lane, Jennifer, Lange, Christian, Larmouth, Emmanuel, Le Doux, Joseph Diffo, Leasure, Elizabeth, Leasure, Katherine, LeBreton, Mat, Lee, Jimmy, Lee, Helen, Lee, Mei Ho, Leno, Amara, Li, Hongying, Liang, Eliza, Liang, Neal, Lim, Dorothy, Lipkin, W. Ian, Liu, Jun, Lo, Modou Moustafa, Lojivis, Leonoris, Long, Nguyen Van, Lucas, Ashley, Lukusa, Jean Paul, Lungay, Victor, Lushima, Shongo, Lutwama, Julius, Ma, Wenjun, Machalaba, Catherine, Maganga, Grace, Magesa, Walter Simon, Mahmoud, Sara H., Makuwa, Maria, Makweta, Asha, Mamun, Abdullah Al, Manandhar, Prajwol, Maneeorn, Patarapol, Mann, Harjeet, Maomy, Bhele, Maptue, Victorine, Mathew, Alice, Mavoungou, Yanne Vanessa, Maw, Min Thein, Mazet, Jonna, Mbala, Placide, Mbuba, Emmanuel, Mbunwe, Eric, McIver, David, Mendelsohn, Emma, Miegakanda, Valchy Bel-Bebi, Minh, Phan Quang, Mkali, Happy, Moatasim, Yassmin, Mombouli, Jean Vivien, Monagin, Corina, Montecino-Latorre, Diego, Mossoun Mossoun, Arsene, Mostafa, Ahmed, Mouiche, Moctar, Mpassi, Romain Bagamboula, Msigwa, Alphonce, Mudakikwa, Antoine, Mugok, Laura Benedict, Mulembakani, Prime, Murray, Suzan, Musa, Fakhrul Hatta, Musabimana, Pacifique, Mutura, Samson, Mwamlima, Tunu, Mwanzanilla, Mwokozi, Myaing, Tin Tin, Myat, Theingi Win, Myo Chit, Aung, N’faly, Magassouba, N’Guettia, Manzan Jean, N’télo, Anatole, Nakimera, Sylivia, Nam, Vu Sinh, Napit, Rajindra, Nathan, Senthilvel K. S. S., Navarrete-Macias, Isamara, Ndebe, Kortu M., Ndiaye, Amadou, Ndiaye, Daouda, Negash, Yohannes, Nga, Nguyen Thi Thanh, Ngay, Ipos, Ngoc, Pham Thi Bich, Niama, Fabien, Nina, Rock Aimé, Niyonzima, Schadrack, Nkom, Felix, Nkoua, Cynthia, Noordin, Noorliza, Noviana, Rachmitasari, Nwobegahay, Julius, Nziza, Julius, O’Rourke, Daniel, O’Rourke, Tammie, Obodai, Evangeline, Okello Okwir, Ricky, Olival, Kevin, Olson, Sarah, Olva, Onkirotin Dionne, Ontiveros, Victoria, Opook, Fernandes, Panchadcharam, Chandrawathani, Pandit, Pranav, Parra, Henri-Joseph, Phuc, Tran Minh, Phuong, Nguyen Thanh, Poultolnor, Jackson Y., Pradhan, Saman, Preston, Eunah Cho, Pruvot, Mathieu, Purevtseren, Dulam, Puri, Dhiraj, Quang, Le Tin Vinh, Rachmitasari, Novie, Rahman, Kaisar, Rahman, Mizanur, Rahman, Mohammed Ziaur, Rahman, Mustafizur, Ramirez, Diana, Randhawa, Nistara, Raut, Samita, Rosario, Joseph, Ross, Albert, Ross, Noam, Rostal, Melinda, Roualdes, Pamela, Rubin, Eddy, Rumi, Aftab Uddin, Rundi, Christina, Sackie, Melkor, Sajali, Zikankuba, Samuels, Sandra G, Sango, Mathias, Saptu, Ammar Rafidah, Saraka, Daniel N’guessan, Sartee, Alvis A., Sayandouno, Sia Alida, Seck, Mame Cheikh, Sedor, Victoria, Sharma, Ajay Narayan, Sharminie, Velsri, Shehata, Mahmoud M., Sheikh, Gafur, Shi, Zhengli, Shiilegdamba, Enkhtuvshin, Shrestha, Bishwo, Shrestha, Rima, Sidibey, Mohammed, Silithammavong, Soubanh, Simon, Daniel, Sion, Emilly, Sipangkui, Symphorosa, Sitam, Frankie Thomas, Smith, Brett, Smith, Bridgette, Smith, Woutrina, Sodnom, Batsikhan, Ssebide, Benard, Suleiman, Maria, Sullivan, Ava, Sungif, Nur Amirah, Suu-Ire, Richard, Sy, Mouhamed, Takuo, Jean Michel, Talafha, Hani, Tamoufe, Ubald, Tetteh, Emmanuel, Than Toe, Aung, Thanda, Lanash, Thanh Long, Ngo, Thein, Wai Zin, Theppangna, Watthana, Thinh, Nguyen Duc, Thuy, Hoang Bich, Thuy, Nguyen Thu, Togami, Eri, Tolno, Moise Bendoua, Tolovou, Kevin, Topani, Rahmat, Tremeau-Bravard, Alexandre, Trupin, Ian, Tumushime, Jean Claude, Tun, Kyaw Yan Naing, Turay, Joseph, Uddin, Helal, Uhart, Marcela, Ureda, Nicole, Valitutto, Marc, Verasahib, Khebir, Vodzak, Megan, Wacharapluesadee, Supaporn, Wahad, Mohammad Yuery Wazlan Abdul, Watson, Brooke, Wells, Heather, White, Allison, Willoughby, Anna, Wiyatno, Ageng, Wolking, David, Yang, Xinglou, Yao, Lim Ming, Yombouno, Sayon, Young, Cristin, Zambrana-Torrelio, Carlos, Zeid, Zahidah Izzati, Zghoul, Ghadeer, Zhang, Libiao, Zhang, Yunzhi, Zhu, Guangjian, Zimmerman, Dawn, Zoumarou, Daba, Aguirre, Alonso, Aguirre, Luis, Akongo, Mark-Joel, Alandia Robles, Erika, Ambu, Laurentius, Ayala Aguilar, Glenda, Barcena, Luis, Barradas, Rosario, Basir, Misliah Mohamad, Bogich, Tiffany, Bounga, Gerard, Buchy, Philippe, Bunn, David, Byaruba, Denis, Cameron, Ken, Carroll, Dennis, Cavero, Nancy, Cespedes, Manuel, Che, Xiaoyu, Chiu, Charles, Chor, Kimashalen, Clements, Andrew, Dary Acevedo, Luz, de Almeida Campos, Angelica, De La Puente, Micaela, de Lamballerie, Xavier, de Paula, Catia, Delwart, Eric, Diffo Le Doux, Joseph, Doyle-Capitman, Catherine, Durigon, Edison, Fair, Joseph, Ferrer-Paris, José R., Formenty, Pierre, Galarza, Isabel, Garcia, Joel, Grard, Gilda, Greatorex, Zoe, Harris, Laurie, Hitchens, Peta, Ho, Mei, Hosseini, Parviez, In, Samath, Iñíguez, Volga, Jain, Komal, Jamaluddin, Abd. Aziz, Johnson, Christine, Jones, Kate, Joyner, Priscilla, Kaba, Serge, Kambale, Eddy, Kataregga, Abdulhameed, Kelly, Terra, Khammavong, Kongsy, Kilpatrick, A. Marm, Laimun, Samsir, Lee, Mei-Ho, LeRoy, Eric, Levinson, Jordan, Levy, Marc, Limachi, Rolando, Loh, Elizabeth, Lowenstine, Linda J., Luis Mollericona, José, Maganga, Ruth, Malakalinga, Joseph, Manhas, Melissa, Marra, Pete, Mbabazi, Rachael, Medellín, Rodrigo, Mendoza, Patricia, Miller, Sireeda, Miranda, Flavia, Mitchell, Megan, Mohamed, Ramlan, Mollard, Debbie, Morse, Stephen, Mouellet, Wivine, Moya, Isabel, Murillo, Yovanna, Murray, Kris, Muyembe Tamfum, Jean-Jacques, Nassar, Fernando, Nathan, Sen, Nsengimana, Olivier, Ogg Keatts, Lucy, Ojeda-Flore, Rafael, Okwir Okello, Ricky, Ondzie, Alain, Paweska, Janusz, Pereira, Alisa, Pereira, Victoria, Perez, Alberto, Perez, Jocelyn, Phon, Simorn, Reed, Patricia, Rejmanek, Dan, Rico, Oscar, Rivera, Rosario, Romero, Monica, Roy, Celina, Saepuloh, Uus, Schneider, Brad, Schwind, Jessica, Singhalath, Sinpakhome, Smith, Kristine, Suárez, Fabiola, Suzan, Gerardo, Thanh Nga, Nguyen Thi, Thomas, Kate, Ticona, Herminio, VanWormer, Elizabeth, Villar, Sandra, Weisman, Wendy, Westfall, Michael, Whittier, Chris, Wicker, Leanne, Wolfe, Nathan, Yang, Angela, Zariquiey, Carlos, Zhang, Shu-Yi, Zorine Nkouants, Baudelaire, Zainuddin, Zainal, Chrisman, Cara, Pabst, August, Shek, Amalhin, and Trostle, Murray

Subjects
0301 basic medicine, Behavioral risk, medicine.medical_specialty, Behavioural sciences, Disease, Community integration, Basic Behavioral and Social Science, 03 medical and health sciences, 0302 clinical medicine, Clinical Research, 2.3 Psychological, Behavioral and Social Science, Agency (sociology), medicine, 2.2 Factors relating to the physical environment, GE1-350, Aetiology, One health, Social science research, business.industry, Prevention, Research, Public health, PREDICT Consortium, Public relations, Focus group, Environmental sciences, Good Health and Well Being, 030104 developmental biology, One Health, Multi-disciplinary surveillance, social and economic factors, Public aspects of medicine, RA1-1270, Infection, Psychology, International development, business, 030217 neurology & neurosurgery
Abstract

In an effort to strengthen global capacity to prevent, detect, and control infectious diseases in animals and people, the United States Agency for International Development’s (USAID) Emerging Pandemic Threats (EPT) PREDICT project funded development of regional, national, and local One Health capacities for early disease detection, rapid response, disease control, and risk reduction. From the outset, the EPT approach was inclusive of social science research methods designed to understand the contexts and behaviors of communities living and working at human-animal-environment interfaces considered high-risk for virus emergence. Using qualitative and quantitative approaches, PREDICT behavioral research aimed to identify and assess a range of socio-cultural behaviors that could be influential in zoonotic disease emergence, amplification, and transmission. This broad approach to behavioral risk characterization enabled us to identify and characterize human activities that could be linked to the transmission dynamics of new and emerging viruses. This paper provides a discussion of implementation of a social science approach within a zoonotic surveillance framework. We conducted in-depth ethnographic interviews and focus groups to better understand the individual- and community-level knowledge, attitudes, and practices that potentially put participants at risk for zoonotic disease transmission from the animals they live and work with, across 6 interface domains. When we asked highly-exposed individuals (ie. bushmeat hunters, wildlife or guano farmers) about the risk they perceived in their occupational activities, most did not perceive it to be risky, whether because it was normalized by years (or generations) of doing such an activity, or due to lack of information about potential risks. Integrating the social sciences allows investigations of the specific human activities that are hypothesized to drive disease emergence, amplification, and transmission, in order to better substantiate behavioral disease drivers, along with the social dimensions of infection and transmission dynamics. Understanding these dynamics is critical to achieving health security--the protection from threats to health-- which requires investments in both collective and individual health security. Involving behavioral sciences into zoonotic disease surveillance allowed us to push toward fuller community integration and engagement and toward dialogue and implementation of recommendations for disease prevention and improved health security. Supplementary Information The online version contains supplementary material available at 10.1186/s42522-021-00036-9.

Published
2021

16. Discrete choice analysis of Oklahoma fishing license holders, fishing trip demand model for eastern Oklahoma natural streams, and fishing trip demand model for fresh water bodies in Oklahoma

Author

Negash, Yohannes A.

Abstract

A disaggregated logistic regression procedure for analysis of qualitative fishing trip data to different types of water bodies in Oklahoma and for different license type holders. The procedure uses the maximum likelihood method to estimate the parameters of the discrete choice model. Results of the study are useful in determining the degree of importance of the different water bodies for fishing by different license type holders. It is also useful in predicting what proportion of new license buyers will make fishing trips to the various water bodies.

Published
1999

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