40 results on '"Gurung, H."'
Search Results
2. Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations
- Author
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Sherratt, K., Gruson, H., Grah, R., Johnson, H., Niehus, R., Prasse, B., Sandmann, F., Deuschel, J., Wolffram, D., Abbott, S., Ullrich, A., Gibson, G., Ray, E. L., Reich, N. G., Sheldon, D., Wang, Y., Wattanachit, N., Wang, L., Trnka, J., Obozinski, G., Sun, T., Thanou, D., Pottier, L., Krymova, E., Meinke, J. H., Barbarossa, M. V., Leithäuser, N., Mohring, J., Schneider, J., Wlazlo, J., Fuhrmann, J., Lange, B., Rodiah, I., Baccam, P., Gurung, H., Stage, S., Suchoski, B., Budzinski, J., Walraven, R., Villanueva, I., Tucek, V., Smíd, M., Zajícek, M., Pérez Alvarez, C., Reina, B., Bosse, N. I., Meakin, S., Castro, L., Fairchild, G., Michaud, I., Osthus, D., Alaimo Di Loro, P., Maruotti, A., Eclerová, V., Kraus, A., Kraus, D., Pribylova, L., Dimitris, B., Li, M. L., Saksham, S., Dehning, J., Mohr, S., Priesemann, V., Redlarski, G., Bejar, B., Ardenghi, G., Parolini, N., Ziarelli, G., Bock, Wolfgang, Heyder, S., Hotz, T., E. Singh, D., Guzman-Merino, M., Aznarte, J. L., Moriña, D., Alonso, S., Alvarez, E., López, D., Prats, C., Burgard, J. P., Rodloff, A., Zimmermann, T., Kuhlmann, A., Zibert, J., Pennoni, F., Divino, F., Català, M., Lovison, G., Giudici, P., Tarantino, B., Bartolucci, F., Jona Lasinio, G., Mingione, M., Farcomeni, A., Srivastava, A., Montero-Manso, P., Adiga, A., Hurt, B., Lewis, B., Marathe, M., Porebski, P., Venkatramanan, S., Bartczuk, R., Dreger, F., Gambin, A., Gogolewski, K., Gruziel-S?omka, M., Krupa, B., Moszynski, A., Niedzielewski, K., Nowosielski, J., Radwan, M., Rakowski, F., Semeniuk, M., Szczurek, E., Zieli?ski, J., Kisielewski, J., Pabjan, B., Kheifetz, Y., Kirsten, H., Scholz, M., Biecek, P., Bodych, M., Filinski, M., Idzikowski, R., Krueger, T., Ozanski, T., Bracher, J., Funk, S., Sherratt, K., Gruson, H., Grah, R., Johnson, H., Niehus, R., Prasse, B., Sandmann, F., Deuschel, J., Wolffram, D., Abbott, S., Ullrich, A., Gibson, G., Ray, E. L., Reich, N. G., Sheldon, D., Wang, Y., Wattanachit, N., Wang, L., Trnka, J., Obozinski, G., Sun, T., Thanou, D., Pottier, L., Krymova, E., Meinke, J. H., Barbarossa, M. V., Leithäuser, N., Mohring, J., Schneider, J., Wlazlo, J., Fuhrmann, J., Lange, B., Rodiah, I., Baccam, P., Gurung, H., Stage, S., Suchoski, B., Budzinski, J., Walraven, R., Villanueva, I., Tucek, V., Smíd, M., Zajícek, M., Pérez Alvarez, C., Reina, B., Bosse, N. I., Meakin, S., Castro, L., Fairchild, G., Michaud, I., Osthus, D., Alaimo Di Loro, P., Maruotti, A., Eclerová, V., Kraus, A., Kraus, D., Pribylova, L., Dimitris, B., Li, M. L., Saksham, S., Dehning, J., Mohr, S., Priesemann, V., Redlarski, G., Bejar, B., Ardenghi, G., Parolini, N., Ziarelli, G., Bock, Wolfgang, Heyder, S., Hotz, T., E. Singh, D., Guzman-Merino, M., Aznarte, J. L., Moriña, D., Alonso, S., Alvarez, E., López, D., Prats, C., Burgard, J. P., Rodloff, A., Zimmermann, T., Kuhlmann, A., Zibert, J., Pennoni, F., Divino, F., Català, M., Lovison, G., Giudici, P., Tarantino, B., Bartolucci, F., Jona Lasinio, G., Mingione, M., Farcomeni, A., Srivastava, A., Montero-Manso, P., Adiga, A., Hurt, B., Lewis, B., Marathe, M., Porebski, P., Venkatramanan, S., Bartczuk, R., Dreger, F., Gambin, A., Gogolewski, K., Gruziel-S?omka, M., Krupa, B., Moszynski, A., Niedzielewski, K., Nowosielski, J., Radwan, M., Rakowski, F., Semeniuk, M., Szczurek, E., Zieli?ski, J., Kisielewski, J., Pabjan, B., Kheifetz, Y., Kirsten, H., Scholz, M., Biecek, P., Bodych, M., Filinski, M., Idzikowski, R., Krueger, T., Ozanski, T., Bracher, J., and Funk, S.
- Abstract
Methods: We used open-source tools to develop a public European COVID-19 Forecast Hub. We invited groups globally to contribute weekly forecasts for COVID-19 cases and deaths reported by a standardised source for 32 countries over the next 1–4 weeks. Teams submitted forecasts from March 2021 using standardised quantiles of the predictive distribution. Each week we created an ensemble forecast, where each predictive quantile was calculated as the equally-weighted average (initially the mean and then from 26th July the median) of all individual models’ predictive quantiles. We measured the performance of each model using the relative Weighted Interval Score (WIS), comparing models’ forecast accuracy relative to all other models. We retrospectively explored alternative methods for ensemble forecasts, including weighted averages based on models’ past predictive performance. Results: Over 52 weeks, we collected forecasts from 48 unique models. We evaluated 29 models’ forecast scores in comparison to the ensemble model. We found a weekly ensemble had a consistently strong performance across countries over time. Across all horizons and locations, the ensemble performed better on relative WIS than 83% of participating models’ forecasts of incident cases (with a total N=886 predictions from 23 unique models), and 91% of participating models’ forecasts of deaths (N=763 predictions from 20 models). Across a 1–4 week time horizon, ensemble performance declined with longer forecast periods when forecasting cases, but remained stable over 4 weeks for incident death forecasts. In every forecast across 32 countries, the ensemble outperformed most contributing models when forecasting either cases or deaths, frequently outperforming all of its individual component models. Among several choices of ensemble methods we found that the most influential and best choice was to use a median average of models instead of using the mean, regardless of methods of weighting component forecast mod
- Published
- 2023
- Full Text
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3. Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations
- Author
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Sherratt, K, Gruson, H, Grah, R, Johnson, H, Niehus, R, Prasse, B, Sandmann, F, Deuschel, J, Wolffram, D, Abbott, S, Ullrich, A, Gibson, G, L Ray, E, G Reich, N, Sheldon, D, Wang, Y, Wattanachit, N, Wang, L, Trnka, J, Obozinski, G, Sun, T, Thanou, D, Pottier, L, Krymova, E, H Meinke, J, Vittoria Barbarossa, M, Leithäuser, N, Mohring, J, Schneider, J, Włazło, J, Fuhrmann, J, Lange, B, Rodiah, I, Baccam, P, Gurung, H, Stage, S, Suchoski, B, Budzinski, J, Walraven, R, Villanueva, I, Tucek, V, Smid, M, Zajíček, M, Pérez Álvarez, C, Reina, B, I Bosse, N, R Meakin, S, Castro, L, Fairchild, G, Michaud, I, Osthus, D, Alaimo Di Loro, P, Maruotti, A, Eclerová, V, Kraus, A, Kraus, D, Pribylova, L, Dimitris, B, Lingzhi Li, M, Saksham, S, Dehning, J, Mohr, S, Priesemann, V, Redlarski, G, Bejar, B, Ardenghi, G, Parolini, N, Ziarelli, G, Bock, W, Heyder, S, Hotz, T, E Singh, D, Guzman-Merino, M, L Aznarte, J, Moriña, D, Alonso, S, Álvarez, E, López, D, Prats, C, Pablo Burgard, J, Rodloff, A, Zimmermann, T, Kuhlmann, A, Zibert, J, Pennoni, F, Divino, F, Català, M, Lovison, G, Giudici, P, Tarantino, B, Bartolucci, F, Jona Lasinio, G, Mingione, M, Farcomeni, A, Srivastava, A, Montero-Manso, P, Adiga, A, Hurt, B, Lewis, B, Marathe, M, Porebski, P, Venkatramanan, S, P Bartczuk, R, Dreger, F, Gambin, A, Gogolewski, K, Gruziel-Słomka, M, Krupa, B, Moszyński, A, Niedzielewski, K, Nowosielski, J, Radwan, M, Rakowski, F, Semeniuk, M, Szczurek, E, Zieliński, J, Kisielewski, J, Pabjan, B, Kirsten, H, Kheifetz, Y, Scholz, M, Biecek, P, Bodych, M, Filinski, M, Idzikowski, R, Krueger, T, Ozanski, T, Bracher, J, Funk, S, Katharine Sherratt, Hugo Gruson, Rok Grah, Helen Johnson, Rene Niehus, Bastian Prasse, Frank Sandmann, Jannik Deuschel, Daniel Wolffram, Sam Abbott, Alexander Ullrich, Graham Gibson, Evan L Ray, Nicholas G Reich, Daniel Sheldon, Yijin Wang, Nutcha Wattanachit, Lijing Wang, Jan Trnka, Guillaume Obozinski, Tao Sun, Dorina Thanou, Loic Pottier, Ekaterina Krymova, Jan H Meinke, Maria Vittoria Barbarossa, Neele Leithäuser, Jan Mohring, Johanna Schneider, Jaroslaw Włazło, Jan Fuhrmann, Berit Lange, Isti Rodiah, Prasith Baccam, Heidi Gurung, Steven Stage, Bradley Suchoski, Jozef Budzinski, Robert Walraven, Inmaculada Villanueva, Vit Tucek, Martin Smid, Milan Zajíček, Cesar Pérez Álvarez, Borja Reina, Nikos I Bosse, Sophie R Meakin, Lauren Castro, Geoffrey Fairchild, Isaac Michaud, Dave Osthus, Pierfrancesco Alaimo Di Loro, Antonello Maruotti, Veronika Eclerová, Andrea Kraus, David Kraus, Lenka Pribylova, Bertsimas Dimitris, Michael Lingzhi Li, Soni Saksham, Jonas Dehning, Sebastian Mohr, Viola Priesemann, Grzegorz Redlarski, Benjamin Bejar, Giovanni Ardenghi, Nicola Parolini, Giovanni Ziarelli, Wolfgang Bock, Stefan Heyder, Thomas Hotz, David E Singh, Miguel Guzman-Merino, Jose L Aznarte, David Moriña, Sergio Alonso, Enric Álvarez, Daniel López, Clara Prats, Jan Pablo Burgard, Arne Rodloff, Tom Zimmermann, Alexander Kuhlmann, Janez Zibert, Fulvia Pennoni, Fabio Divino, Marti Català, Gianfranco Lovison, Paolo Giudici, Barbara Tarantino, Francesco Bartolucci, Giovanna Jona Lasinio, Marco Mingione, Alessio Farcomeni, Ajitesh Srivastava, Pablo Montero-Manso, Aniruddha Adiga, Benjamin Hurt, Bryan Lewis, Madhav Marathe, Przemyslaw Porebski, Srinivasan Venkatramanan, Rafal P Bartczuk, Filip Dreger, Anna Gambin, Krzysztof Gogolewski, Magdalena Gruziel-Słomka, Bartosz Krupa, Antoni Moszyński, Karol Niedzielewski, Jedrzej Nowosielski, Maciej Radwan, Franciszek Rakowski, Marcin Semeniuk, Ewa Szczurek, Jakub Zieliński, Jan Kisielewski, Barbara Pabjan, Holger Kirsten, Yuri Kheifetz, Markus Scholz, Przemyslaw Biecek, Marcin Bodych, Maciej Filinski, Radoslaw Idzikowski, Tyll Krueger, Tomasz Ozanski, Johannes Bracher, Sebastian Funk, Sherratt, K, Gruson, H, Grah, R, Johnson, H, Niehus, R, Prasse, B, Sandmann, F, Deuschel, J, Wolffram, D, Abbott, S, Ullrich, A, Gibson, G, L Ray, E, G Reich, N, Sheldon, D, Wang, Y, Wattanachit, N, Wang, L, Trnka, J, Obozinski, G, Sun, T, Thanou, D, Pottier, L, Krymova, E, H Meinke, J, Vittoria Barbarossa, M, Leithäuser, N, Mohring, J, Schneider, J, Włazło, J, Fuhrmann, J, Lange, B, Rodiah, I, Baccam, P, Gurung, H, Stage, S, Suchoski, B, Budzinski, J, Walraven, R, Villanueva, I, Tucek, V, Smid, M, Zajíček, M, Pérez Álvarez, C, Reina, B, I Bosse, N, R Meakin, S, Castro, L, Fairchild, G, Michaud, I, Osthus, D, Alaimo Di Loro, P, Maruotti, A, Eclerová, V, Kraus, A, Kraus, D, Pribylova, L, Dimitris, B, Lingzhi Li, M, Saksham, S, Dehning, J, Mohr, S, Priesemann, V, Redlarski, G, Bejar, B, Ardenghi, G, Parolini, N, Ziarelli, G, Bock, W, Heyder, S, Hotz, T, E Singh, D, Guzman-Merino, M, L Aznarte, J, Moriña, D, Alonso, S, Álvarez, E, López, D, Prats, C, Pablo Burgard, J, Rodloff, A, Zimmermann, T, Kuhlmann, A, Zibert, J, Pennoni, F, Divino, F, Català, M, Lovison, G, Giudici, P, Tarantino, B, Bartolucci, F, Jona Lasinio, G, Mingione, M, Farcomeni, A, Srivastava, A, Montero-Manso, P, Adiga, A, Hurt, B, Lewis, B, Marathe, M, Porebski, P, Venkatramanan, S, P Bartczuk, R, Dreger, F, Gambin, A, Gogolewski, K, Gruziel-Słomka, M, Krupa, B, Moszyński, A, Niedzielewski, K, Nowosielski, J, Radwan, M, Rakowski, F, Semeniuk, M, Szczurek, E, Zieliński, J, Kisielewski, J, Pabjan, B, Kirsten, H, Kheifetz, Y, Scholz, M, Biecek, P, Bodych, M, Filinski, M, Idzikowski, R, Krueger, T, Ozanski, T, Bracher, J, Funk, S, Katharine Sherratt, Hugo Gruson, Rok Grah, Helen Johnson, Rene Niehus, Bastian Prasse, Frank Sandmann, Jannik Deuschel, Daniel Wolffram, Sam Abbott, Alexander Ullrich, Graham Gibson, Evan L Ray, Nicholas G Reich, Daniel Sheldon, Yijin Wang, Nutcha Wattanachit, Lijing Wang, Jan Trnka, Guillaume Obozinski, Tao Sun, Dorina Thanou, Loic Pottier, Ekaterina Krymova, Jan H Meinke, Maria Vittoria Barbarossa, Neele Leithäuser, Jan Mohring, Johanna Schneider, Jaroslaw Włazło, Jan Fuhrmann, Berit Lange, Isti Rodiah, Prasith Baccam, Heidi Gurung, Steven Stage, Bradley Suchoski, Jozef Budzinski, Robert Walraven, Inmaculada Villanueva, Vit Tucek, Martin Smid, Milan Zajíček, Cesar Pérez Álvarez, Borja Reina, Nikos I Bosse, Sophie R Meakin, Lauren Castro, Geoffrey Fairchild, Isaac Michaud, Dave Osthus, Pierfrancesco Alaimo Di Loro, Antonello Maruotti, Veronika Eclerová, Andrea Kraus, David Kraus, Lenka Pribylova, Bertsimas Dimitris, Michael Lingzhi Li, Soni Saksham, Jonas Dehning, Sebastian Mohr, Viola Priesemann, Grzegorz Redlarski, Benjamin Bejar, Giovanni Ardenghi, Nicola Parolini, Giovanni Ziarelli, Wolfgang Bock, Stefan Heyder, Thomas Hotz, David E Singh, Miguel Guzman-Merino, Jose L Aznarte, David Moriña, Sergio Alonso, Enric Álvarez, Daniel López, Clara Prats, Jan Pablo Burgard, Arne Rodloff, Tom Zimmermann, Alexander Kuhlmann, Janez Zibert, Fulvia Pennoni, Fabio Divino, Marti Català, Gianfranco Lovison, Paolo Giudici, Barbara Tarantino, Francesco Bartolucci, Giovanna Jona Lasinio, Marco Mingione, Alessio Farcomeni, Ajitesh Srivastava, Pablo Montero-Manso, Aniruddha Adiga, Benjamin Hurt, Bryan Lewis, Madhav Marathe, Przemyslaw Porebski, Srinivasan Venkatramanan, Rafal P Bartczuk, Filip Dreger, Anna Gambin, Krzysztof Gogolewski, Magdalena Gruziel-Słomka, Bartosz Krupa, Antoni Moszyński, Karol Niedzielewski, Jedrzej Nowosielski, Maciej Radwan, Franciszek Rakowski, Marcin Semeniuk, Ewa Szczurek, Jakub Zieliński, Jan Kisielewski, Barbara Pabjan, Holger Kirsten, Yuri Kheifetz, Markus Scholz, Przemyslaw Biecek, Marcin Bodych, Maciej Filinski, Radoslaw Idzikowski, Tyll Krueger, Tomasz Ozanski, Johannes Bracher, and Sebastian Funk
- Abstract
Background: Short-term forecasts of infectious disease contribute to situational awareness and capacity planning. Based on best practice in other fields and recent insights in infectious disease epidemiology, one can maximise forecasts’ predictive performance by combining independent models into an ensemble. Here we report the performance of ensemble predictions of COVID-19 cases and deaths across Europe from March 2021 to March 2022. Methods: We created the European COVID-19 Forecast Hub, an online open-access platform where modellers upload weekly forecasts for 32 countries with results publicly visualised and evaluated. We created a weekly ensemble forecast from the equally-weighted average across individual models’ predictive quantiles. We measured forecast accuracy using a baseline and relative Weighted Interval Score (rWIS). We retrospectively explored ensemble methods, including weighting by past performance. Results: We collected weekly forecasts from 48 models, of which we evaluated 29 models alongside the ensemble model. The ensemble had a consistently strong performance across countries over time, performing better on rWIS than 91% of forecasts for deaths (N=763 predictions from 20 models), and 83% forecasts for cases (N=886 predictions from 23 models). Performance remained stable over a 4-week horizon for death forecasts but declined with longer horizons for cases. Among ensemble methods, the most influential choice came from using a median average instead of the mean, regardless of weighting component models. Conclusions: Our results support combining independent models into an ensemble forecast to improve epidemiological predictions, and suggest that median averages yield better performance than methods based on means. We highlight that forecast consumers should place more weight on incident death forecasts than case forecasts at horizons greater than two weeks. Funding: European Commission, Ministerio de Ciencia, Innovación y Universidades, FEDER; Ag
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- 2023
4. Self-sensing SMA Actuator Using Extended Kalman Filter and Artificial Neural Network
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Gurung, H. and Banerjee, A.
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- 2016
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5. Preloading and instrumentation for Rockhampton lakes creek road waste transfer station
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International Congress on Environmental Geotechnics (7th : 2014 : Melbourne, Vic.), Gong, R, Gurung, H, Charlesworth, G, and Cossins, R
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- 2014
6. The role of Indigenous Peoples and local communities in assessment of forest condition, pressures and conservation actions at key forest sites in tropical Asia and New Guinea.
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CROWE, O., CROSBY, M., DE LA PAZ, M. B. E., DE LA ROSA JR, G. E., GOWAE, G. Y., GURUNG, H., KUMPEL, N., HANI, T., LEE, E. L., PANOPIO, J. K., PERUMAL, B., QUIMPO, J. D. G., RESURRECCION, N. A., SATYAL, P., SIMKINS, A. T., THOMAS, J., WIDYANTO, A., and YEAP, C. A.
- Subjects
TROPICAL forests ,INDIGENOUS peoples ,FOREST biodiversity ,COMMUNITY forests ,FOREST monitoring ,IMPORTANT bird areas - Abstract
Copyright of International Forestry Review is the property of Commonwealth Forestry Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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- 2023
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7. Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations
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Sherratt, K., primary, Gruson, H., additional, Grah, R., additional, Johnson, H., additional, Niehus, R., additional, Prasse, B., additional, Sandman, F., additional, Deuschel, J., additional, Wolffram, D., additional, Abbott, S., additional, Ullrich, A., additional, Gibson, G., additional, Ray, EL., additional, Reich, NG., additional, Sheldon, D., additional, Wang, Y., additional, Wattanachit, N., additional, Wang, L., additional, Trnka, J., additional, Obozinski, G., additional, Sun, T., additional, Thanou, D., additional, Pottier, L., additional, Krymova, E., additional, Barbarossa, MV., additional, Leithäuser, N., additional, Mohring, J., additional, Schneider, J., additional, Wlazlo, J., additional, Fuhrmann, J., additional, Lange, B., additional, Rodiah, I., additional, Baccam, P., additional, Gurung, H., additional, Stage, S., additional, Suchoski, B., additional, Budzinski, J., additional, Walraven, R., additional, Villanueva, I., additional, Tucek, V., additional, Šmíd, M., additional, Zajícek, M., additional, Pérez Álvarez, C., additional, Reina, B., additional, Bosse, NI., additional, Meakin, S., additional, Di Loro, P. Alaimo, additional, Maruotti, A., additional, Eclerová, V., additional, Kraus, A., additional, Kraus, D., additional, Pribylova, L., additional, Dimitris, B., additional, Li, ML., additional, Saksham, S., additional, Dehning, J., additional, Mohr, S., additional, Priesemann, V., additional, Redlarski, G., additional, Bejar, B., additional, Ardenghi, G., additional, Parolini, N., additional, Ziarelli, G., additional, Bock, W., additional, Heyder, S., additional, Hotz, T., additional, E. Singh, D., additional, Guzman-Merino, M., additional, Aznarte, JL., additional, Moriña, D., additional, Alonso, S., additional, Álvarez, E., additional, López, D., additional, Prats, C., additional, Burgard, JP., additional, Rodloff, A., additional, Zimmermann, T., additional, Kuhlmann, A., additional, Zibert, J., additional, Pennoni, F., additional, Divino, F., additional, Català, M., additional, Lovison, G., additional, Giudici, P., additional, Tarantino, B., additional, Bartolucci, F., additional, Jona Lasinio, G., additional, Mingione, M., additional, Farcomeni, A., additional, Srivastava, A., additional, Montero-Manso, P., additional, Adiga, A., additional, Hurt, B., additional, Lewis, B., additional, Marathe, M., additional, Porebski, P., additional, Venkatramanan, S., additional, Bartczuk, R., additional, Dreger, F., additional, Gambin, A., additional, Gogolewski, K., additional, Gruziel-Slomka, M., additional, Krupa, B., additional, Moszynski, A., additional, Niedzielewski, K., additional, Nowosielski, J., additional, Radwan, M., additional, Rakowski, F., additional, Semeniuk, M., additional, Szczurek, E., additional, Zielinski, J., additional, Kisielewski, J., additional, Pabjan, B., additional, Holger, K., additional, Kheifetz, Y., additional, Scholz, M., additional, Bodych, M., additional, Filinski, M., additional, Idzikowski, R., additional, Krueger, T., additional, Ozanski, T., additional, Bracher, J., additional, and Funk, S., additional
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- 2022
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8. A partnership approach to the design and use of a quantitative measure: Co-producing and piloting the WASH gender equality measure in Cambodia and Nepal
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Gonzalez, D, Abdel Sattar, R, Budhathoki, R, Carrard, N, Chase, RP, Crawford, J, Halcrow, G, Kozole, T, MacArthur, J, Nicoletti, C, Toeur, V, Basnet, MP, Chhetri, A, Gurung, H, Yadav, A, Vourchnea, P, Willetts, J, Gonzalez, D, Abdel Sattar, R, Budhathoki, R, Carrard, N, Chase, RP, Crawford, J, Halcrow, G, Kozole, T, MacArthur, J, Nicoletti, C, Toeur, V, Basnet, MP, Chhetri, A, Gurung, H, Yadav, A, Vourchnea, P, and Willetts, J
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- 2022
9. A critical review of the outreach research program in technology evaluation, adaptation, and dissemination for rice farmers in Nepal
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Gurung, H. B., Kadayat, K. B., Sah, R. P., Vaidya, A. K., Balasubramanian, V., editor, Ladha, J. K., editor, and Denning, G. L., editor
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- 1999
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10. Experimentally Validated Analytical Modelling of the Laser Bending of Low Carbon Steel Sheets.
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NAIR, R., KANT, R., YADAV, R., and GURUNG, H.
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MILD steel ,SHEET-steel ,WORKPIECES ,LASERS - Abstract
Laser bending is used to induce precise bend angle in sheets and foils by controlled laser irradiations. In this paper an analytical model is developed to set up a relationship between bend angle and process parameters in terms of laser power and scan speed during laser sheet bending. The proposed model estimates the bend angle considering laser parameters and thermo-mechanical properties of the workpiece material. The analytical model is validated by conducting experiments on low carbon steel, and the results showed that the developed analytical model is in good agreement with the experimental results. The average absolute error between experimental and analytical bend angle is about 9.51%. [ABSTRACT FROM AUTHOR]
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- 2022
11. Pokhara Valley, Nepal Himalaya : a field study in regional geography
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Gurung, H. B.
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910.9 - Published
- 1965
12. Development and Analysis of Laser-assisted Bending with Moving Pre-displacement Load.
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KANT, R., GURUNG, H., SARMA, U., and JOSHI, S. N.
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LIVE loads , *FINITE element method , *TEMPERATURE distribution , *STRAINS & stresses (Mechanics) - Abstract
A novel and an efficient technique is developed in this work to obtain a large bend angle with a single laser scan. The proposed technique consists of laser-assisted bending with moving pre-displacement. A thermomechanical model is developed for laser-assisted bending with moving pre-displacement using the finite element method (FEM). The numerical model is validated with the developed experimental results and is found to be in good agreement with the experimental bend angle. The validated model is used to analyse the effect of pre-displacement on temperature distribution, stress-strain distribution, bend angle, edge effect and springback. Laser-assisted bending is compared with the laser bending process without external mechanical load. Results showed that the bend angle, edge effect and springback are significantly affected by the applied predisplacement. [ABSTRACT FROM AUTHOR]
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- 2021
13. Influence of Mother Rhizome Retrieval and Organic Treatment of the Retrieved Plants on Economic Yield of Ginger at Salyan, Nepal
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Basnet, M., primary and Gurung, H., primary
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- 2018
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14. Fibroblast growth factor-2 drives and maintains progressive corneal neovascularization following HSV-1 infection
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Gurung, H R, primary, Carr, M M, additional, Bryant, K, additional, Chucair-Elliott, A J, additional, and Carr, D JJ, additional
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- 2018
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15. Landscape Change in the Nepal Hills: Evidence from Lamjung
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Gurung, H., primary
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- 2004
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16. On the Map: The Mountains of Asia (Supplement: Maps and Table)
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Gurung, H., primary
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- 1999
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17. Mountains of Asia: A Regional Inventory
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Gurung, H., primary
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- 1999
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18. Self-sensing Shape Memory Alloy wire actuator based on Unscented Kalman Filter
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Gurung, H., primary and Banerjee, A., additional
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- 2016
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19. Development of an extended Kalman filter for the self-sensing application of a spring-biased shape memory alloy wire actuator
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Gurung, H, primary and Banerjee, A, additional
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- 2016
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20. Development of an Extended Kalman Filter for Self-Sensing Application of Shape Memory Alloy Wire Actuator
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Gurung, H., primary, Karmakar, S., additional, and Banerjee, A., additional
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- 2015
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21. Ophthalmia Neonatorum: A Letter to Editor
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Bhatta, Dharm Raj, primary, Ansari, M T, primary, Gokhale, Shishir, primary, Ghosh, A N, primary, De, J K, primary, and Gurung, H M, primary
- Published
- 2012
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22. Real time state of charge prediction using Kalman Filter.
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Choudhury, J.R., Banerjee, T.P., Gurung, H., Bhattacharjee, A.K., and Das, S.
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- 2009
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23. A pedo-transfer function (PTF) for estimating soil bulk density from basic soil data and its comparison with existing PTFs
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Kaur, Ravinder, primary, Kumar, Sanjeev, additional, and Gurung, H. P., additional
- Published
- 2002
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24. The proximate and flavanol contents of some fodder tree leaves
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Acamovic, T, primary, Gurung, H B, additional, and Stewart, C S, additional
- Published
- 1992
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25. Nepal: The Kingdom in the Himalayas T. Hagen
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Gurung, H. B.
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- 1962
- Full Text
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26. Development and analysis of laser-assisted bending with moving pre-displacement load
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Ravi Kant, Gurung, H., Sarma, U., and Joshi, S. N.
27. Experimentally Validated Analytical Modelling of the Laser Bending of Low Carbon Steel Sheets
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Nair, R., Kant, R., Ramsingh Yadav, and Gurung, H.
28. Himalayan Pilgrimage D. Snellgrove
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Gurung, H. B.
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- 1962
- Full Text
- View/download PDF
29. Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations
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Katharine Sherratt, Hugo Gruson, Rok Grah, Helen Johnson, Rene Niehus, Bastian Prasse, Frank Sandmann, Jannik Deuschel, Daniel Wolffram, Sam Abbott, Alexander Ullrich, Graham Gibson, Evan L Ray, Nicholas G Reich, Daniel Sheldon, Yijin Wang, Nutcha Wattanachit, Lijing Wang, Jan Trnka, Guillaume Obozinski, Tao Sun, Dorina Thanou, Loic Pottier, Ekaterina Krymova, Jan H Meinke, Maria Vittoria Barbarossa, Neele Leithäuser, Jan Mohring, Johanna Schneider, Jaroslaw Włazło, Jan Fuhrmann, Berit Lange, Isti Rodiah, Prasith Baccam, Heidi Gurung, Steven Stage, Bradley Suchoski, Jozef Budzinski, Robert Walraven, Inmaculada Villanueva, Vit Tucek, Martin Smid, Milan Zajíček, Cesar Pérez Álvarez, Borja Reina, Nikos I Bosse, Sophie R Meakin, Lauren Castro, Geoffrey Fairchild, Isaac Michaud, Dave Osthus, Pierfrancesco Alaimo Di Loro, Antonello Maruotti, Veronika Eclerová, Andrea Kraus, David Kraus, Lenka Pribylova, Bertsimas Dimitris, Michael Lingzhi Li, Soni Saksham, Jonas Dehning, Sebastian Mohr, Viola Priesemann, Grzegorz Redlarski, Benjamin Bejar, Giovanni Ardenghi, Nicola Parolini, Giovanni Ziarelli, Wolfgang Bock, Stefan Heyder, Thomas Hotz, David E Singh, Miguel Guzman-Merino, Jose L Aznarte, David Moriña, Sergio Alonso, Enric Álvarez, Daniel López, Clara Prats, Jan Pablo Burgard, Arne Rodloff, Tom Zimmermann, Alexander Kuhlmann, Janez Zibert, Fulvia Pennoni, Fabio Divino, Marti Català, Gianfranco Lovison, Paolo Giudici, Barbara Tarantino, Francesco Bartolucci, Giovanna Jona Lasinio, Marco Mingione, Alessio Farcomeni, Ajitesh Srivastava, Pablo Montero-Manso, Aniruddha Adiga, Benjamin Hurt, Bryan Lewis, Madhav Marathe, Przemyslaw Porebski, Srinivasan Venkatramanan, Rafal P Bartczuk, Filip Dreger, Anna Gambin, Krzysztof Gogolewski, Magdalena Gruziel-Słomka, Bartosz Krupa, Antoni Moszyński, Karol Niedzielewski, Jedrzej Nowosielski, Maciej Radwan, Franciszek Rakowski, Marcin Semeniuk, Ewa Szczurek, Jakub Zieliński, Jan Kisielewski, Barbara Pabjan, Holger Kirsten, Yuri Kheifetz, Markus Scholz, Przemyslaw Biecek, Marcin Bodych, Maciej Filinski, Radoslaw Idzikowski, Tyll Krueger, Tomasz Ozanski, Johannes Bracher, Sebastian Funk, Sherratt, K, Gruson, H, Grah, R, Johnson, H, Niehus, R, Prasse, B, Sandmann, F, Deuschel, J, Wolffram, D, Abbott, S, Ullrich, A, Gibson, G, L Ray, E, G Reich, N, Sheldon, D, Wang, Y, Wattanachit, N, Wang, L, Trnka, J, Obozinski, G, Sun, T, Thanou, D, Pottier, L, Krymova, E, H Meinke, J, Vittoria Barbarossa, M, Leithäuser, N, Mohring, J, Schneider, J, Włazło, J, Fuhrmann, J, Lange, B, Rodiah, I, Baccam, P, Gurung, H, Stage, S, Suchoski, B, Budzinski, J, Walraven, R, Villanueva, I, Tucek, V, Smid, M, Zajíček, M, Pérez Álvarez, C, Reina, B, I Bosse, N, R Meakin, S, Castro, L, Fairchild, G, Michaud, I, Osthus, D, Alaimo Di Loro, P, Maruotti, A, Eclerová, V, Kraus, A, Kraus, D, Pribylova, L, Dimitris, B, Lingzhi Li, M, Saksham, S, Dehning, J, Mohr, S, Priesemann, V, Redlarski, G, Bejar, B, Ardenghi, G, Parolini, N, Ziarelli, G, Bock, W, Heyder, S, Hotz, T, E Singh, D, Guzman-Merino, M, L Aznarte, J, Moriña, D, Alonso, S, Álvarez, E, López, D, Prats, C, Pablo Burgard, J, Rodloff, A, Zimmermann, T, Kuhlmann, A, Zibert, J, Pennoni, F, Divino, F, Català, M, Lovison, G, Giudici, P, Tarantino, B, Bartolucci, F, Jona Lasinio, G, Mingione, M, Farcomeni, A, Srivastava, A, Montero-Manso, P, Adiga, A, Hurt, B, Lewis, B, Marathe, M, Porebski, P, Venkatramanan, S, P Bartczuk, R, Dreger, F, Gambin, A, Gogolewski, K, Gruziel-Słomka, M, Krupa, B, Moszyński, A, Niedzielewski, K, Nowosielski, J, Radwan, M, Rakowski, F, Semeniuk, M, Szczurek, E, Zieliński, J, Kisielewski, J, Pabjan, B, Kirsten, H, Kheifetz, Y, Scholz, M, Biecek, P, Bodych, M, Filinski, M, Idzikowski, R, Krueger, T, Ozanski, T, Bracher, J, and Funk, S
- Subjects
epidemiology ,global health ,none ,General Immunology and Microbiology ,General Neuroscience ,mathematical modeling ,COVID-19 ,infectious diseases forecatsting ,General Medicine ,udc:616 ,General Biochemistry, Genetics and Molecular Biology ,COVID-19, Countries Predictions, Infectious disease, Multivariate Statistical Models, Short-term forecasts ,udc:616-036.22:519.876.5 ,SECS-S/01 - STATISTICA ,infectious diseases forecatsting, epidemiology, mathematical modeling, capacity planning, COVID-19, combining independent models, ensemble forecast ,ensemble forecast ,Settore SECS-S/01 ,napovedovanje nalezljivih bolezni, epidemiologija, matematično modeliranje, načrtovanje zmogljivosti, COVID-19, kombiniranje neodvisnih modelov, skupna napoved ,ddc:600 ,capacity planning ,combining independent models - Abstract
eLife 12, e81916 (2023). doi:10.7554/eLife.81916, Background:Short-term forecasts of infectious disease burden can contribute to situational awareness and aid capacity planning. Based on best practice in other fields and recent insights in infectious disease epidemiology, one can maximise the predictive performance of such forecasts if multiple models are combined into an ensemble. Here, we report on the performance of ensembles in predicting COVID-19 cases and deaths across Europe between 08 March 2021 and 07 March 2022.Methods:We used open-source tools to develop a public European COVID-19 Forecast Hub. We invited groups globally to contribute weekly forecasts for COVID-19 cases and deaths reported by a standardised source for 32 countries over the next 1–4 weeks. Teams submitted forecasts from March 2021 using standardised quantiles of the predictive distribution. Each week we created an ensemble forecast, where each predictive quantile was calculated as the equally-weighted average (initially the mean and then from 26th July the median) of all individual models’ predictive quantiles. We measured the performance of each model using the relative Weighted Interval Score (WIS), comparing models’ forecast accuracy relative to all other models. We retrospectively explored alternative methods for ensemble forecasts, including weighted averages based on models’ past predictive performance.Results:Over 52 weeks, we collected forecasts from 48 unique models. We evaluated 29 models’ forecast scores in comparison to the ensemble model. We found a weekly ensemble had a consistently strong performance across countries over time. Across all horizons and locations, the ensemble performed better on relative WIS than 83% of participating models’ forecasts of incident cases (with a total N=886 predictions from 23 unique models), and 91% of participating models’ forecasts of deaths (N=763 predictions from 20 models). Across a 1–4 week time horizon, ensemble performance declined with longer forecast periods when forecasting cases, but remained stable over 4 weeks for incident death forecasts. In every forecast across 32 countries, the ensemble outperformed most contributing models when forecasting either cases or deaths, frequently outperforming all of its individual component models. Among several choices of ensemble methods we found that the most influential and best choice was to use a median average of models instead of using the mean, regardless of methods of weighting component forecast models.Conclusions:Our results support the use of combining forecasts from individual models into an ensemble in order to improve predictive performance across epidemiological targets and populations during infectious disease epidemics. Our findings further suggest that median ensemble methods yield better predictive performance more than ones based on means. Our findings also highlight that forecast consumers should place more weight on incident death forecasts than incident case forecasts at forecast horizons greater than 2 weeks., Published by eLife Sciences Publications, Cambridge
- Published
- 2023
30. Seasonal Hyperacute Panuveitis (SHAPU) Outbreak Amidst COVID-19 Pandemic.
- Author
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Gurung H, Kharel Sitaula R, Karki P, Lamichhane G, Singh S, Shrestha E, Khatri A, Banstola A, Joshi P, Dahal HN, Sharma AK, Joshi SN, and Prasad Upadhaya M
- Subjects
- Humans, Male, Female, Cross-Sectional Studies, Child, Adolescent, Adult, Middle Aged, Eye Infections, Viral epidemiology, Eye Infections, Viral diagnosis, Disease Outbreaks, Child, Preschool, Young Adult, Aged, Animals, Pandemics, Moths, Acute Disease, COVID-19 epidemiology, Panuveitis epidemiology, Panuveitis diagnosis, SARS-CoV-2, Seasons
- Abstract
Purpose: To document the demographic profile of the SHAPU outbreak amidst the COVID-19 pandemic., Methods: A multicentric cross-sectional study of the 2021 SHAPU outbreak during the second phase of the COVID-19 outbreak., Results: A total of 135 patients were diagnosed with SHAPU from August to December 2021, 77 (57%) were children <16 years, males 54.8% and 34.8% had direct physical contact with white moths and 41.5% had severe type of SHAPU. Dramatic increment in the moth abundance was noted in these outbreak sites. Few cases presented with atypical ocular findings, unlike past outbreaks. Due to the ongoing COVID-19 pandemic with restrictions on travel and transportation, timely management was difficult and good visual outcome was achieved only in mild-moderate cases with an early presentation., Conclusion: The surge in the number of SHAPU patients, its occurrence in areas previously unreported, and some atypical presentation added raised suspicion of a possible link between COVID-19 and SHAPU.
- Published
- 2024
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31. Challenges of COVID-19 Case Forecasting in the US, 2020-2021.
- Author
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Lopez VK, Cramer EY, Pagano R, Drake JM, O'Dea EB, Adee M, Ayer T, Chhatwal J, Dalgic OO, Ladd MA, Linas BP, Mueller PP, Xiao J, Bracher J, Castro Rivadeneira AJ, Gerding A, Gneiting T, Huang Y, Jayawardena D, Kanji AH, Le K, Mühlemann A, Niemi J, Ray EL, Stark A, Wang Y, Wattanachit N, Zorn MW, Pei S, Shaman J, Yamana TK, Tarasewicz SR, Wilson DJ, Baccam S, Gurung H, Stage S, Suchoski B, Gao L, Gu Z, Kim M, Li X, Wang G, Wang L, Wang Y, Yu S, Gardner L, Jindal S, Marshall M, Nixon K, Dent J, Hill AL, Kaminsky J, Lee EC, Lemaitre JC, Lessler J, Smith CP, Truelove S, Kinsey M, Mullany LC, Rainwater-Lovett K, Shin L, Tallaksen K, Wilson S, Karlen D, Castro L, Fairchild G, Michaud I, Osthus D, Bian J, Cao W, Gao Z, Lavista Ferres J, Li C, Liu TY, Xie X, Zhang S, Zheng S, Chinazzi M, Davis JT, Mu K, Pastore Y Piontti A, Vespignani A, Xiong X, Walraven R, Chen J, Gu Q, Wang L, Xu P, Zhang W, Zou D, Gibson GC, Sheldon D, Srivastava A, Adiga A, Hurt B, Kaur G, Lewis B, Marathe M, Peddireddy AS, Porebski P, Venkatramanan S, Wang L, Prasad PV, Walker JW, Webber AE, Slayton RB, Biggerstaff M, Reich NG, and Johansson MA
- Subjects
- Humans, United States epidemiology, Computational Biology, Models, Statistical, COVID-19 epidemiology, COVID-19 transmission, Forecasting methods, SARS-CoV-2, Pandemics statistics & numerical data
- Abstract
During the COVID-19 pandemic, forecasting COVID-19 trends to support planning and response was a priority for scientists and decision makers alike. In the United States, COVID-19 forecasting was coordinated by a large group of universities, companies, and government entities led by the Centers for Disease Control and Prevention and the US COVID-19 Forecast Hub (https://covid19forecasthub.org). We evaluated approximately 9.7 million forecasts of weekly state-level COVID-19 cases for predictions 1-4 weeks into the future submitted by 24 teams from August 2020 to December 2021. We assessed coverage of central prediction intervals and weighted interval scores (WIS), adjusting for missing forecasts relative to a baseline forecast, and used a Gaussian generalized estimating equation (GEE) model to evaluate differences in skill across epidemic phases that were defined by the effective reproduction number. Overall, we found high variation in skill across individual models, with ensemble-based forecasts outperforming other approaches. Forecast skill relative to the baseline was generally higher for larger jurisdictions (e.g., states compared to counties). Over time, forecasts generally performed worst in periods of rapid changes in reported cases (either in increasing or decreasing epidemic phases) with 95% prediction interval coverage dropping below 50% during the growth phases of the winter 2020, Delta, and Omicron waves. Ideally, case forecasts could serve as a leading indicator of changes in transmission dynamics. However, while most COVID-19 case forecasts outperformed a naïve baseline model, even the most accurate case forecasts were unreliable in key phases. Further research could improve forecasts of leading indicators, like COVID-19 cases, by leveraging additional real-time data, addressing performance across phases, improving the characterization of forecast confidence, and ensuring that forecasts were coherent across spatial scales. In the meantime, it is critical for forecast users to appreciate current limitations and use a broad set of indicators to inform pandemic-related decision making., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: APP report grants from Metabiota Inc outside the submitted work. J.S. and Columbia University declare partial ownership of SK Analytics. No other authors have competing interests to declare., (Copyright: This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.)
- Published
- 2024
- Full Text
- View/download PDF
32. Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations.
- Author
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Sherratt K, Gruson H, Grah R, Johnson H, Niehus R, Prasse B, Sandmann F, Deuschel J, Wolffram D, Abbott S, Ullrich A, Gibson G, Ray EL, Reich NG, Sheldon D, Wang Y, Wattanachit N, Wang L, Trnka J, Obozinski G, Sun T, Thanou D, Pottier L, Krymova E, Meinke JH, Barbarossa MV, Leithauser N, Mohring J, Schneider J, Wlazlo J, Fuhrmann J, Lange B, Rodiah I, Baccam P, Gurung H, Stage S, Suchoski B, Budzinski J, Walraven R, Villanueva I, Tucek V, Smid M, Zajicek M, Perez Alvarez C, Reina B, Bosse NI, Meakin SR, Castro L, Fairchild G, Michaud I, Osthus D, Alaimo Di Loro P, Maruotti A, Eclerova V, Kraus A, Kraus D, Pribylova L, Dimitris B, Li ML, Saksham S, Dehning J, Mohr S, Priesemann V, Redlarski G, Bejar B, Ardenghi G, Parolini N, Ziarelli G, Bock W, Heyder S, Hotz T, Singh DE, Guzman-Merino M, Aznarte JL, Morina D, Alonso S, Alvarez E, Lopez D, Prats C, Burgard JP, Rodloff A, Zimmermann T, Kuhlmann A, Zibert J, Pennoni F, Divino F, Catala M, Lovison G, Giudici P, Tarantino B, Bartolucci F, Jona Lasinio G, Mingione M, Farcomeni A, Srivastava A, Montero-Manso P, Adiga A, Hurt B, Lewis B, Marathe M, Porebski P, Venkatramanan S, Bartczuk RP, Dreger F, Gambin A, Gogolewski K, Gruziel-Slomka M, Krupa B, Moszyński A, Niedzielewski K, Nowosielski J, Radwan M, Rakowski F, Semeniuk M, Szczurek E, Zielinski J, Kisielewski J, Pabjan B, Holger K, Kheifetz Y, Scholz M, Przemyslaw B, Bodych M, Filinski M, Idzikowski R, Krueger T, Ozanski T, Bracher J, and Funk S
- Subjects
- Humans, Forecasting, Models, Statistical, Retrospective Studies, Communicable Diseases, COVID-19 diagnosis, COVID-19 epidemiology, Epidemics
- Abstract
Background: Short-term forecasts of infectious disease burden can contribute to situational awareness and aid capacity planning. Based on best practice in other fields and recent insights in infectious disease epidemiology, one can maximise the predictive performance of such forecasts if multiple models are combined into an ensemble. Here, we report on the performance of ensembles in predicting COVID-19 cases and deaths across Europe between 08 March 2021 and 07 March 2022., Methods: We used open-source tools to develop a public European COVID-19 Forecast Hub. We invited groups globally to contribute weekly forecasts for COVID-19 cases and deaths reported by a standardised source for 32 countries over the next 1-4 weeks. Teams submitted forecasts from March 2021 using standardised quantiles of the predictive distribution. Each week we created an ensemble forecast, where each predictive quantile was calculated as the equally-weighted average (initially the mean and then from 26th July the median) of all individual models' predictive quantiles. We measured the performance of each model using the relative Weighted Interval Score (WIS), comparing models' forecast accuracy relative to all other models. We retrospectively explored alternative methods for ensemble forecasts, including weighted averages based on models' past predictive performance., Results: Over 52 weeks, we collected forecasts from 48 unique models. We evaluated 29 models' forecast scores in comparison to the ensemble model. We found a weekly ensemble had a consistently strong performance across countries over time. Across all horizons and locations, the ensemble performed better on relative WIS than 83% of participating models' forecasts of incident cases (with a total N=886 predictions from 23 unique models), and 91% of participating models' forecasts of deaths (N=763 predictions from 20 models). Across a 1-4 week time horizon, ensemble performance declined with longer forecast periods when forecasting cases, but remained stable over 4 weeks for incident death forecasts. In every forecast across 32 countries, the ensemble outperformed most contributing models when forecasting either cases or deaths, frequently outperforming all of its individual component models. Among several choices of ensemble methods we found that the most influential and best choice was to use a median average of models instead of using the mean, regardless of methods of weighting component forecast models., Conclusions: Our results support the use of combining forecasts from individual models into an ensemble in order to improve predictive performance across epidemiological targets and populations during infectious disease epidemics. Our findings further suggest that median ensemble methods yield better predictive performance more than ones based on means. Our findings also highlight that forecast consumers should place more weight on incident death forecasts than incident case forecasts at forecast horizons greater than 2 weeks., Funding: AA, BH, BL, LWa, MMa, PP, SV funded by National Institutes of Health (NIH) Grant 1R01GM109718, NSF BIG DATA Grant IIS-1633028, NSF Grant No.: OAC-1916805, NSF Expeditions in Computing Grant CCF-1918656, CCF-1917819, NSF RAPID CNS-2028004, NSF RAPID OAC-2027541, US Centers for Disease Control and Prevention 75D30119C05935, a grant from Google, University of Virginia Strategic Investment Fund award number SIF160, Defense Threat Reduction Agency (DTRA) under Contract No. HDTRA1-19-D-0007, and respectively Virginia Dept of Health Grant VDH-21-501-0141, VDH-21-501-0143, VDH-21-501-0147, VDH-21-501-0145, VDH-21-501-0146, VDH-21-501-0142, VDH-21-501-0148. AF, AMa, GL funded by SMIGE - Modelli statistici inferenziali per governare l'epidemia, FISR 2020-Covid-19 I Fase, FISR2020IP-00156, Codice Progetto: PRJ-0695. AM, BK, FD, FR, JK, JN, JZ, KN, MG, MR, MS, RB funded by Ministry of Science and Higher Education of Poland with grant 28/WFSN/2021 to the University of Warsaw. BRe, CPe, JLAz funded by Ministerio de Sanidad/ISCIII. BT, PG funded by PERISCOPE European H2020 project, contract number 101016233. CP, DL, EA, MC, SA funded by European Commission - Directorate-General for Communications Networks, Content and Technology through the contract LC-01485746, and Ministerio de Ciencia, Innovacion y Universidades and FEDER, with the project PGC2018-095456-B-I00. DE., MGu funded by Spanish Ministry of Health / REACT-UE (FEDER). DO, GF, IMi, LC funded by Laboratory Directed Research and Development program of Los Alamos National Laboratory (LANL) under project number 20200700ER. DS, ELR, GG, NGR, NW, YW funded by National Institutes of General Medical Sciences (R35GM119582; the content is solely the responsibility of the authors and does not necessarily represent the official views of NIGMS or the National Institutes of Health). FB, FP funded by InPresa, Lombardy Region, Italy. HG, KS funded by European Centre for Disease Prevention and Control. IV funded by Agencia de Qualitat i Avaluacio Sanitaries de Catalunya (AQuAS) through contract 2021-021OE. JDe, SMo, VP funded by Netzwerk Universitatsmedizin (NUM) project egePan (01KX2021). JPB, SH, TH funded by Federal Ministry of Education and Research (BMBF; grant 05M18SIA). KH, MSc, YKh funded by Project SaxoCOV, funded by the German Free State of Saxony. Presentation of data, model results and simulations also funded by the NFDI4Health Task Force COVID-19 (https://www.nfdi4health.de/task-force-covid-19-2) within the framework of a DFG-project (LO-342/17-1). LP, VE funded by Mathematical and Statistical modelling project (MUNI/A/1615/2020), Online platform for real-time monitoring, analysis and management of epidemic situations (MUNI/11/02202001/2020); VE also supported by RECETOX research infrastructure (Ministry of Education, Youth and Sports of the Czech Republic: LM2018121), the CETOCOEN EXCELLENCE (CZ.02.1.01/0.0/0.0/17-043/0009632), RECETOX RI project (CZ.02.1.01/0.0/0.0/16-013/0001761). NIB funded by Health Protection Research Unit (grant code NIHR200908). SAb, SF funded by Wellcome Trust (210758/Z/18/Z)., Competing Interests: KS, HG, RG, HJ, RN, BP, FS, JD, DW, SA, AU, GG, ER, NR, DS, YW, NW, LW, JT, GO, TS, DT, LP, EK, JM, MB, NL, JM, JS, JW, JF, BL, IR, JB, RW, IV, VT, MS, MZ, CP, BR, NB, SM, LC, GF, IM, DO, PA, AM, VE, AK, DK, LP, BD, ML, SS, JD, SM, VP, GR, BB, GA, NP, GZ, WB, SH, TH, DS, MG, JA, DM, SA, EA, DL, CP, JB, AR, TZ, AK, JZ, FP, FD, MC, GL, PG, BT, FB, GJ, MM, AF, AS, PM, AA, BH, BL, MM, PP, SV, RB, FD, AG, KG, MG, BK, AM, KN, JN, MR, FR, MS, ES, JZ, JK, BP, KH, YK, MS, BP, MB, MF, RI, TK, TO, JB, SF No competing interests declared, PB, HG, SS, BS Affiliated with IEM, Inc. The author has no financial interests to declare
- Published
- 2023
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33. Classification of Seasonal Hyperacute Panuveitis (SHAPU).
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Kharel Sitaula R, Khatri A, Karki P, Joshi SN, Gurung H, Shrestha E, Maharjan I, Sharma AK, and Upadhyay MP
- Subjects
- Humans, Nepal epidemiology, Uveitis classification, Uveitis diagnosis
- Abstract
Introduction: Classification are essential part of scientific methodology and has important role in medical reporting system. Even after having 46 years long history, Seasonal Hyperacute Panuveitis (SHAPU), the blinding diseases reported mainly from Nepal lacks a standard classification system. Thus, we aim to contribute in the ophthalmic nosology by purposing a classification system for SHAPU., Methodology: The classification is suggested on the background of prolonged experience of this entity by the group of investigators who have dedicated years of research on this topic., Conclusion: We are optimistic that the proposed classification system will help in effective planning and evaluation of this ocular emergency condition and deliver the appropriate and reliable information for timely management and prevention of complications.
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- 2022
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34. Sporadic summer outbreak of SHAPU in even years: Does the pattern match with the usual autumn outbreak?
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Gurung H, Kharel Sitaula R, Karki P, Khatri A, Khanal B, Joshi SN, Maharjan I, and Upadhyay MP
- Abstract
Purpose: Seasonal Hyperacute Panuveitis (SHAPU), is a mysterious blinding disease seen only in Nepal with a higher prevalence among children usually seen in autumn every alternate odd year since 1975. This report highlights the sporadic summer outbreak in the even years with atypical presentation., Observations: Three patients were diagnosed as SHAPU in the summer (May) of 2020. All of them noted the presence of white moths (Gazalina species) in their environment with or without direct physical contact. The clinical patterns were severe in nature including corneal melting. Two out of three patients (66.6%) developed phthisis bulbi and lost their vision., Conclusions and Importance: White moth has been associated as a risk factor for SHAPU. Despite the known natural history of appearance after monsoon of every odd year, the few unhatched eggs of the moths may hatch under the favorable circumstances in the summer of the even years and may lead to the sporadic outbreak of SHAPU. Though less in numbers, the clinical presentation of such sporadic SHAPU cases may be atypical with less favorable outcome., Competing Interests: No conflict of interest exists., (© 2021 Published by Elsevier Inc.)
- Published
- 2021
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35. Challenges of Pediatric Cataract Surgery in a Case of Seasonal Hyperacute Panuveitis (SHAPU).
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Karki P, Sitaula RK, Khatri A, Joshi SN, Gurung H, Maharjan I, Sharma AK, and Upadhaya MP
- Abstract
A four-year-old female child diagnosed as a case of severe Seasonal Hyperacute Panuveitis (SHAPU) underwent lens-sparing core vitrectomy in her left eye with intravitreal antibiotic and steroid. Patient responded well to treatment and intraocular inflammation subsided. However, three months later, she developed vision impairing dense cataract which also made posterior segment assessment difficult. Lens aspiration with primary posterior capsulotomy and anterior vitrectomy with intraocular lens (IOL) implantation was performed. However, four weeks later, the patient developed occlusio pupillae with iris bombe. She did not respond to medical management so synechiolysis with surgical iridectomy was performed after which a normal depth anterior chamber was attained. Synechia and iris bombe were also relieved, and vision was regained., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2021 Pratap Karki et al.)
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- 2021
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36. The Risk Factors of Seasonal Hyperacute Panuveitis.
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Upadhyay MP, Kharel Sitaula R, Manandhar A, Gower EW, Karki P, Gurung H, Maharjan I, Reuben S, Karmacharya BM, and Joshi SN
- Subjects
- Animals, Case-Control Studies, Female, Humans, Male, Risk Factors, Seasons, Butterflies, Panuveitis
- Abstract
Background: Seasonal Hyperacute Panuveitis (SHAPU) is an eye disease of unclear aetiology occurring cyclically during the autumn in odd years in Nepal causing blindness within a week. This study is the first of its type to investigate the risk factors of SHAPU., Methods: A multicentric national level case-control study was performed during the 2017 SHAPU outbreak. Cases were matched to controls in a 1:3 ratio based on age, sex and geographic area.Questionnaire-based personal interview was used and risk factors were categorized as biological and behavioral. For univariate analysis, frequency, median and interquartile range was calculated. Chi-squared test with/without continuity correction and Fisher's exact test were used. Multivariate conditional logistic regressions were used for all the independent variables for p <0.1 in the univariate analyses., Results: We identified 35 cases and 105 controls; 71.4% were children≤16 years (38-day infant to 50-year-old). All were immunocompetent individuals, males were 57.1% and females 42.9%. Potential risks such as visible moths/butterfly activity, contact with livestock, and attending mass gatherings of people were not reported more frequently in cases vs controls in univariate analyses. Differences in possibly protective factors such as self-reported mosquito net use, light off at night while sleeping, and habit of hands/face washing after physical contact/touch with any insects/butterflies/birds were not statistically significant between both groups. In multivariate model, SHAPU cases were significantly more likely than controls to report physical contact with butterflies/white moths (Adjusted OR:6.89; CI:2.79-17.01, p < .001)., Conclusions: Direct physical contact with butterflies/moths was associated with significantly increased odds of SHAPU cases.
- Published
- 2021
- Full Text
- View/download PDF
37. Assessing Profile and Treatment Outcome in Patients of Ocular Surface Squamous Neoplasia (OSSN).
- Author
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Shrestha E, Banstola L, Man I, Gurung B, Gurung H, and Adhikari HB
- Subjects
- Adolescent, Adult, Amnion transplantation, Carcinoma in Situ epidemiology, Carcinoma in Situ surgery, Carcinoma, Squamous Cell epidemiology, Carcinoma, Squamous Cell surgery, Conjunctival Neoplasms epidemiology, Conjunctival Neoplasms surgery, Female, Humans, Male, Middle Aged, Nepal epidemiology, Ophthalmologic Surgical Procedures, Retrospective Studies, Treatment Outcome, Visual Acuity physiology, Young Adult, Carcinoma in Situ pathology, Carcinoma, Squamous Cell pathology, Conjunctival Neoplasms pathology, Neoplasm Recurrence, Local diagnosis
- Abstract
Purpose: This study has attempted to assess the profile and treatment outcome of patients with OSSN and report the recurrence rate following treatment of primary OSSN., Materials and Methods: This was a retrospective, non-comparative hospital based observational study. The case files of OSSN patients from Jan 2010 to Dec 2017were assessed. The study site was Himalaya Eye Hospital, Gharipatan, Pokhara. This hospital is the only eye hospital in province 4, Nepal. All the files of suspicious cases of conjunctival mass or OSSN were reviewed. Data regarding demographic characteristics, vision acuity, involved eye, clinical features, conjunctival lesion type, duration of symptoms, preoperative clinical diagnosis, history, other eye disease, associated systemic diseases, occupation as well as treatment modalities of the patients and its outcome were taken., Results: Out of 40 cases, females were 21(52.5%) vesus 19(47.5%) male. Amongthem, 19 patients had keratinized stratified squamous epithelium, 17 had conjunctiva intraepithelial squamous neoplasia and 4 had invasive squalors cell carcinoma. Predominance age group was 15-59 age (25/40, 62.5%), among which 12 had keratinized stratified squamous epithelium, 11 conjunctival intraepithelial squamousneoplasia and 2 had invasive squamous cell carcinoma conjunctival lesion. The mean duration of symptoms was about nine months (S.D. = 5.972). The mean size of lesion mass was 3.28 mm (SD=1.569). More than half of the cases (55%, 22/40) had the mass located at nasal limbus. 95% (38/40) cases underwent lesion total excision and direct closure and 5% (2/40) underwent lesion excision followed by Amniotic Membrane grafting. Only 5% (2/40) cases, those who had undergone lesion excision, the lesion recurrence was observed during follow up period of 31 months., Conclusion: The outcome of the OSSN following the treatment was found to be satisfactory. Further studies with prolonged follow-up are recommended to evaluate the risk of recurrence and other adverse effects., (© NEPjOPH.)
- Published
- 2019
- Full Text
- View/download PDF
38. Labial frenum tear from instrumental delivery.
- Author
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Gurung H and Cohn A
- Subjects
- Female, Humans, Infant, Newborn, Male, Obstetrical Forceps adverse effects, Pregnancy, Birth Injuries etiology, Extraction, Obstetrical adverse effects, Labial Frenum injuries
- Published
- 2015
- Full Text
- View/download PDF
39. Evaluating the effectiveness of conservation site networks under climate change: accounting for uncertainty.
- Author
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Bagchi R, Crosby M, Huntley B, Hole DG, Butchart SH, Collingham Y, Kalra M, Rajkumar J, Rahmani A, Pandey M, Gurung H, Trai le T, Van Quang N, and Willis SG
- Subjects
- Biodiversity, Models, Theoretical, Climate Change, Conservation of Natural Resources, Uncertainty
- Abstract
We forecasted potential impacts of climate change on the ability of a network of key sites for bird conservation (Important Bird Areas; IBAs) to provide suitable climate for 370 bird species of current conservation concern in two Asian biodiversity hotspots: the Eastern Himalaya and Lower Mekong. Comparable studies have largely not accounted for uncertainty, which may lead to inappropriate conclusions. We quantified the contribution of four sources of variation (choice of general circulation models, emission scenarios and species distribution modelling methods and variation in species distribution data) to uncertainty in forecasts and tested if our projections were robust to these uncertainties. Declines in the availability of suitable climate within the IBA network by 2100 were forecast as 'extremely likely' for 45% of species, whereas increases were projected for only 2%. Thus, we predict almost 24 times as many 'losers' as 'winners'. However, for no species was suitable climate 'extremely likely' to be completely lost from the network. Considerable turnover (median = 43%, 95% CI = 35-69%) in species compositions of most IBAs were projected by 2100. Climatic conditions in 47% of IBAs were projected as 'extremely likely' to become suitable for fewer priority species. However, no IBA was forecast to become suitable for more species. Variation among General Circulation Models and Species Distribution Models contributed most to uncertainty among forecasts. This uncertainty precluded firm conclusions for 53% of species and IBAs because 95% confidence intervals included projections of no change. Considering this uncertainty, however, allows robust recommendations concerning the remaining species and IBAs. Overall, while the IBA network will continue to sustain bird conservation, climate change will modify which species each site will be suitable for. Thus, adaptive management of the network, including modified site conservation strategies and facilitating species' movement among sites, is critical to ensure effective future conservation., (© 2012 Blackwell Publishing Ltd.)
- Published
- 2013
- Full Text
- View/download PDF
40. Need of improvement in timing of prophylactic antibiotic in elective surgery.
- Author
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Shah JN, Maharjan SB, Piya R, Shrestha A, Shrestha K, Basnet NB, and Gurung HM
- Subjects
- Adolescent, Adult, Cross-Sectional Studies, Drug Administration Schedule, Female, Humans, Male, Nepal, Practice Guidelines as Topic, Prospective Studies, Time Factors, Anti-Bacterial Agents administration & dosage, Antibiotic Prophylaxis standards, Cefazolin administration & dosage, Surgical Wound Infection prevention & control
- Abstract
Introduction: Infections in surgery are major concern of morbidity, mortality, and costs. Timely antibiotic prophylaxis before incision ensures optimum concentration of AP in blood and tissues to prevent surgical site infections . However, proper timing of AP remains problematic as reported by various studies, though none so far from Nepal. Aim of this prospective observational study was to assess and address the issues for improvements in timing of AP., Methods: The pattern of antibiotic prophylaxis were prospectively collected in respect to time of induction, and incision time were recorded on predesigned 'AP form'. The study included all the elective major cases who received AP of intravenous Cefazoline 1 g as per our existing protocol. The emergency surgeries and obstetric cases were excluded from the study., Results: There were 125 cases of which 89% received AP before incision (63% within 5 minutes before incision), while 11% had AP after the incision and 1% within the recommended time period of 2 hour to 30 minutes before incision., Conclusions: Current practice of antibiotic prophylaxis (AP) needs improvement as per standard guidelines of AP within 2 hour to 30 before incision.
- Published
- 2010
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